132 Lstm Jobs

#GenerativeAI Experts ready for AI race, this is for you! Announcing Hexaware conducting Walkin Drive for AI Engineer and Lead Data Scientist (GenAI)-Pune Location-12th July 2025(Saturday) Interested candidates share your CV at umaparvathyc@hexaware.com Open Positions: AI Engineer/Lead Data Scientist (GenAI) AI Engineer Experience- 3+years Lead Data Scientist (GenAI) Experience- 7+years Notice Period- 15 days/30days Max (who serving Notice Period) Walkin Drive Location- Pune Date of drive- 12th July 2025(Saturday) Must have Experience: LLM, Advance RAG, NLP, transformer model, LangChain Technical Skill: 1. Strong Experience in Data Scientist (GENAI) 2. Proficiency with Generative AI models like GANs, VAEs, and transformers 3. Expertise with cloud platforms (AWS, Azure, Google Cloud) for deploying AI models 4. Strong Python Fast API experience, SDA based implementations for all the APIs 5. Knowledge of Agentic AI concepts and applications

Line of Service Advisory Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager Job Description & Summary At PwC, our people in business application consulting specialise in consulting services for a variety of business applications, helping clients optimise operational efficiency. These individuals analyse client needs, implement software solutions, and provide training and support for seamless integration and utilisation of business applications, enabling clients to achieve their strategic objectives. As a business application consulting generalist at PwC, you will provide consulting services for a wide range of business applications. You will leverage a broad understanding of various software solutions to assist clients in optimising operational efficiency through analysis, implementation, training, and support. *Why PWC At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more about us . At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations. " Responsibilities: Position responsibilities and expectations · Designing and building analytical /DL/ ML algorithms using Python, R and other statistical tools. · Strong data representation and lucid presentation (of analysis/modelling output) using Python, R Markdown, Power Point, Excel etc. · Ability to learn new scripting language or analytics platform. Technical Skills required (must have) · HandsOn Exposure to Generative AI (Design, development of GenAI application in production) · Strong understanding of RAG, Vector Database, Lang Chain and multimodal AI applications. · Strong understanding of deploying and optimizing AI application in production. · Strong knowledge of statistical and data mining techniques like Linear & Logistic Regression analysis, Decision trees, Bagging, Boosting, Time Series and Non-parametric analysis. · Strong knowledge of DL & Neural Network Architectures (CNN, RNN, LSTM, Transformers etc.) · Strong knowledge of SQL and R/Python and experience with distribute data/computing tools/IDEs. · Experience in advanced Text Analytics (NLP, NLU, NLG). · Strong hands-on experience of end-to-end statistical model development and implementation · Understanding of LLMOps, ML Ops for scalable ML development. · Basic understanding of DevOps and deployment of models into production (PyTorch, TensorFlow etc.). · Expert level proficiency algorithm building languages like SQL, R and Python and data visualization tools like Shiny, Qlik, Power BI etc. · Exposure to Cloud Platform (Azure or AWS or GCP) technologies and services like Azure AI/ Sage maker/Vertex AI, Auto ML, Azure Index, Azure Functions, OCR, OpenAI, storage, scaling etc. Technical Skills required (Any one or more) · Experience in video/ image analytics (Computer Vision) · Experience in IoT/ machine logs data analysis · Exposure to data analytics platforms like Domino Data Lab, c3.ai, H2O, Alteryx or KNIME · Expertise in Cloud analytics platforms (Azure, AWS or Google) · Experience in Process Mining with expertise in Celonis or other tools · Proven capability in using Generative AI services like OpenAI, Google (Gemini) · Understanding of Agentic AI Framework (Lang Graph, Auto gen etc.) · Understanding of fine-tuning for pre-trained models like GPT, LLaMA, Claude etc. using LoRA, QLoRA and PEFT technique. · Proven capability in building customized models from open-source distributions like Llama, Stable Diffusion Mandatory skill sets: AI chatbots, Data structures, GenAI object-oriented programming, IDE, API, LLM Prompts, Streamlit Preferred skill sets: AI chatbots, Data structures, GenAI object-oriented programming, IDE, API, LLM Prompts, Streamlit Years of experience required: 3-10 Years Education qualification: BE, B. Tech, M. Tech, M. Stat, Ph.D., M.Sc. (Stats / Maths) Education (if blank, degree and/or field of study not specified) Degrees/Field of Study required: Master of Business Administration, Bachelor of Engineering Degrees/Field of Study preferred: Certifications (if blank, certifications not specified) Required Skills Data Science Optional Skills Accepting Feedback, Accepting Feedback, Active Listening, Analytical Reasoning, Analytical Thinking, Application Software, Business Data Analytics, Business Management, Business Technology, Business Transformation, Coaching and Feedback, Communication, Creativity, Documentation Development, Embracing Change, Emotional Regulation, Empathy, Implementation Research, Implementation Support, Implementing Technology, Inclusion, Intellectual Curiosity, Learning Agility, Optimism, Performance Assessment {+ 21 more} Desired Languages (If blank, desired languages not specified) Travel Requirements Not Specified Available for Work Visa Sponsorship? No Government Clearance Required? No Job Posting End Date

Line of Service Advisory Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Senior Associate Job Description & Summary At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions. Why PWC At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more about us . At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations. Job Description & Summary: -Stakeholder facing role across different functions of the organizations (e.g., Marketing, Finance, Logistics, Procurement, Customer, Supply Chain, HR) to develop analyses that lead to actionable insights that accelerate profitable growth. -Working with bigdata, SAP, Oracle, Workday or several other large and complex databases. Responsibilities: -Designing and building analytical / ML algorithms using Python, R and other statistical tools. -Strong data representation and lucid presentation (of analysis/modelling output) using Python, R Markdown, Power Point, Excel etc. -Ability to learn new scripting language or analytics platform Mandatory skill sets: -Strong knowledge of statistical and data mining techniques like Linear & Logistic Regression analysis, Decision trees, Bagging, Boosting, Time Series and Non-parametric analysis. -Strong knowledge of Deep Learning and Neural Network Architectures like CNN, RNN, LSTM, Transformers etc. -Strong knowledge of SQL and R/Python and experience with distribute data/computing tools. -Experience in advanced Text Analytics (NLP, NLU, NLG). -Strong hands-on experience of end-to-end statistical model development and implementation -Basic understanding of MLOps for scalable ML development. -Expert level proficiency algorithm building languages like SQL, R and Python and data visualization tools like Shiny, Qlik, Power BI etc. -Exposure to Cloud technologies (Azure or AWS Preferred skill sets: -Experience in video/ image analytics -Experience in IoT/ machine logs data analysis -Exposure to data analytics platforms like Domino Data Lab, c3.ai, H2O, Alteryx or KNIME -Expertise in Cloud analytics platforms (Azure, AWS or Google) -Experience in Process Mining with expertise in Celonis or other tools Years of experience required: -3 to 10 years' experience in Analytics modeling -Experience in algorithm development -Open for domestic and international travels Education qualification: - M.Sc. / M.Stat/ MS (Statistics / Mathematics/ Economics/ Computer Science or ML/DL focus) -B.Tech / M.Tech (Computer Science, Mathematics & Scientific Computing etc.) -MBA with ML/DL Focus. Education (if blank, degree and/or field of study not specified) Degrees/Field of Study required: Master of Science, Master of Business Administration, Bachelor of Technology Degrees/Field of Study preferred: Certifications (if blank, certifications not specified) Required Skills Data Mining, Statistics Optional Skills Amazon Web Services (AWS), Microsoft Azure Desired Languages (If blank, desired languages not specified) Travel Requirements Not Specified Available for Work Visa Sponsorship? No Government Clearance Required? No Job Posting End Date

Kovai.co is a catalyst, sparking a revolution in the world of enterprise software and B2B SaaS, we are a technology powerhouse delivering best-in-class enterprise software and game-changing SaaS solutions across industries. At Kovai.co , we're rewriting the B2B landscape by empowering over 2,500 businesses worldwide with our award-winning SaaS solutions. Our Products Biztalk360 Turbo360 Document360 “UK headquarters. Indian innovation. Global impact.” Our journey has been nothing short of remarkable, having witnessed exponential growth and profitability right from our inception. We are on track towards $30 million in annual revenue – and we're just getting started. Kovai.co is fueled by a tribe of thoughtful helpers, obsessed with empowering customers, uplifting colleagues, and igniting our own journeys. Redefining tech is our game. Are you in? Join Kovai.co – where passion meets purpose. What's the job: Data Scientist What You’ll Do On The Job Understand business problems and formulate hypothesis Collecting and interpreting data, analyzing results, identifying patterns and trends in large data sets Research and develop advanced statistical and machine learning models for analysis of large-scale, high-dimensional data Build custom machine learning models for various use-cases such as recommendation, insight generation, forecasting, anomaly detection, and so on Performing data quality checks for quality assurance Build data pipelines to supply quality data to machine learning model in production environment Supports innovative analytical thinking & solutions that results into improved business performance Writing reports based on insights gained from exploratory data analysis and able to articulate it to product owner Practice industry well-known Machine Learning Framework and best practices Who’ll be a good fit: Proven working experience of 2+ years as a Data Scientist Strong in mathematics - statistics, probability, linear algebra, time series Proficiency in working with large datasets and data wrangling using SQL (Structured Query Language) Familiar with tools such as JuPyteR, Tensorflow ,Python , Azure ML, Prophet (Open source software), MS SQL Server Working knowledge of applied machine learning techniques such as regression, clustering, forecasting of time-series data and Classification models Proficiency in LSTM, hypothesis testing and supervised learning / unsupervised learning. Familiarity with all aspects of the analytical cycle such as data wrangling and visualisation, feature engineering, and model building Write scalable scripts to fetch or modify data from API endpoints Exposure to cloud technology is preferred (Azure) Excellent critical thinking, verbal, and written communication skills Equal Opportunities Kovai.co is committed to building a workforce that reflects the richness of our society. We believe in fostering a culture of belonging and respect for all. Kovai.co stands firmly against discrimination, ensuring equal opportunity for everyone to build a successful career. Submit Your Application You have successfully applied You have errors in applying Apply With Resume * First Name* Middle Name Last Name* Email* Mobile Phone Social Network and Web Links Provide us with links to see some of your work (Git/ Dribble/ Behance/ Pinterest/ Blog/ Medium) Employer Education

Senior Data Scientist (Deep Learning and Artificial Intelligence) Job Description We aim to bring about a new paradigm in medical image diagnostics; providing intelligent, holistic, ethical, explainable and patient centric care. We are looking for innovative problem solvers who love solving problems. We want people who can empathize with the consumer, understand business problems, and design and deliver intelligent products. People who are looking to extend artificial intelligence into unexplored areas. Your primary focus will be in applying deep learning and artificial intelligence techniques to the domain of medical image analysis. Responsibilities Selecting features, building and optimizing classifier engines using deep learning techniques. Understanding the problem and applying the suitable image processing techniques Use techniques from artificial intelligence/deep learning to solve supervised and unsupervised learning problems. Understanding and designing solutions for complex problems related to medical image analysis by using Deep Learning/Object Detection/Image Segmentation. Recommend and implement best practices around the application of statistical modeling. Create, train, test, and deploy various neural networks to solve complex problems. Develop and implement solutions to fit business problems which may include applying algorithms from a standard statistical tool, deep learning or custom algorithm development. Understanding the requirements and designing solutions and architecture in accordance with them is important. Participate in code reviews, sprint planning, and Agile ceremonies to drive high-quality deliverables. Design and implement scalable data science architectures for training, inference, and deployment pipelines. Ensure code quality, readability, and maintainability by enforcing software engineering best practices within the data science team. Optimize models for production, including quantization, pruning, and latency reduction for real-time inference. Drive the adoption of versioing strategies for models, datasets, and experiments (e.g., using MLFlow, DVC). Contribute to the architectural design of data platforms to support large-scale experimentation and production workloads. Skills and Qualifications Strong software engineering skills in Python (or other languages used in data science) with emphasis on clean code, modularity, and testability. Excellent understanding and hands-on of Deep Learning techniques such as ANN, CNN, RNN, LSTM, Transformers, VAEs etc. Must have experience with Tensorflow or PyTorch framework in building, training, testing, and deploying neural networks. Experience in solving problems in the domain of Computer Vision. Knowledge of data, data augmentation, data curation, and synthetic data generation. Ability to understand the complete problem and design the solutions that best fit all the constraints. Knowledge of the common data science and deep learning libraries and toolkits such as Keras, Pandas, Scikit-learn, Numpy, Scipy, OpenCV etc. Good applied statistical skills, such as distributions, statistical testing, regression, etc. Exposure to Agile/Scrum methodologies and collaborative development practices. Experience with the development of RESTful APIs. The knowledge of libraries like FastAPI and the ability to apply it to deep learning architectures is essential. Excellent analytical and problem-solving skills with a good attitude and keen to adapt to evolving technologies. Experience with medical image analysis will be an advantage. Experience designing and building ML architecture components (e.g., feature stores, model registries, inference servers). Solid understanding of software design patterns, microservices, and cloud-native architectures. Expertise in model optimization techniques (e.g., ONNX conversion, TensorRT, model distillation) Education : BE/B Tech MS/M Tech (will be a bonus) Experience : 3+ Years Job Type: Full-time Ability to commute/relocate: Gurugram, Haryana: Reliably commute or planning to relocate before starting work (Required) Application Question(s): Do you have experience leading teams in AI Development? Do you have experience creating software architecture for production environment in AI applications? Experience: Deep learning: 3 years (Required) Computer vision: 3 years (Required) PyTorch: 3 years (Required) Work Location: In person

Senior Data Scientist (Deep Learning and Artificial Intelligence) Job Description We aim to bring about a new paradigm in medical image diagnostics; providing intelligent, holistic, ethical, explainable and patient centric care. We are looking for innovative problem solvers who love solving problems. We want people who can empathize with the consumer, understand business problems, and design and deliver intelligent products. People who are looking to extend artificial intelligence into unexplored areas. Your primary focus will be in applying deep learning and artificial intelligence techniques to the domain of medical image analysis. Responsibilities Selecting features, building and optimizing classifier engines using deep learning techniques. Understanding the problem and applying the suitable image processing techniques Use techniques from artificial intelligence/deep learning to solve supervised and unsupervised learning problems. Understanding and designing solutions for complex problems related to medical image analysis by using Deep Learning/Object Detection/Image Segmentation. Recommend and implement best practices around the application of statistical modeling. Create, train, test, and deploy various neural networks to solve complex problems. Develop and implement solutions to fit business problems which may include applying algorithms from a standard statistical tool, deep learning or custom algorithm development. Understanding the requirements and designing solutions and architecture in accordance with them is important. Participate in code reviews, sprint planning, and Agile ceremonies to drive high-quality deliverables. Design and implement scalable data science architectures for training, inference, and deployment pipelines. Ensure code quality, readability, and maintainability by enforcing software engineering best practices within the data science team. Optimize models for production, including quantization, pruning, and latency reduction for real-time inference. Drive the adoption of versioing strategies for models, datasets, and experiments (e.g., using MLFlow, DVC). Contribute to the architectural design of data platforms to support large-scale experimentation and production workloads. Skills and Qualifications Strong software engineering skills in Python (or other languages used in data science) with emphasis on clean code, modularity, and testability. Excellent understanding and hands-on of Deep Learning techniques such as ANN, CNN, RNN, LSTM, Transformers, VAEs etc. Must have experience with Tensorflow or PyTorch framework in building, training, testing, and deploying neural networks. Experience in solving problems in the domain of Computer Vision. Knowledge of data, data augmentation, data curation, and synthetic data generation. Ability to understand the complete problem and design the solutions that best fit all the constraints. Knowledge of the common data science and deep learning libraries and toolkits such as Keras, Pandas, Scikit-learn, Numpy, Scipy, OpenCV etc. Good applied statistical skills, such as distributions, statistical testing, regression, etc. Exposure to Agile/Scrum methodologies and collaborative development practices. Experience with the development of RESTful APIs. The knowledge of libraries like FastAPI and the ability to apply it to deep learning architectures is essential. Excellent analytical and problem-solving skills with a good attitude and keen to adapt to evolving technologies. Experience with medical image analysis will be an advantage. Experience designing and building ML architecture components (e.g., feature stores, model registries, inference servers). Solid understanding of software design patterns, microservices, and cloud-native architectures. Expertise in model optimization techniques (e.g., ONNX conversion, TensorRT, model distillation) Education : BE/B Tech MS/M Tech (will be a bonus) Experience : 3+ Years Job Type: Full-time Ability to commute/relocate: Gurugram, Haryana: Reliably commute or planning to relocate before starting work (Required) Application Question(s): Do you have experience leading teams in AI Development? Do you have experience creating software architecture for production environment in AI applications? Experience: Deep learning: 3 years (Required) Computer vision: 3 years (Required) PyTorch: 3 years (Required) Work Location: In person

Experience: 3+ years Requirements: Excellent knowledge of computer vision concepts, including but not limited to Image Classification, Object Detection, and Semantic Segmentation, developed using state-of-the-art deep learning algorithms. Hands-on experience developing efficient and real-time convolutional neural network (CNN) models for computer vision tasks. Strong proficiency in at least one of the deep learning frameworks, such as PyTorch , TensorFlow , or Caffe, with the ability to apply them to computer vision problems. Quick prototyping skills in Python and coding and debugging proficiency in C++ . Good communication and collaboration skills to work effectively in a team and communicate complex technical concepts. Qualifications: A Ph.D. degree (including candidates at various stages of their Ph.D., such as thesis submission, thesis submitted, degree awaited, synopsis seminar completed, defense completed) in Deep Learning with hands-on coding skills and a passion for an industrial career will be preferred. Master's or Bachelor's degree with thorough industrial work experience in developing computer vision applications using deep learning. Postgraduates or Undergraduates with a strong academic background in Deep Learning, Computer Vision, or related fields, and demonstrated coding skills, are also encouraged to apply. Preferred: Publications in top-tier computer vision conferences like CVPR, ICCV, ECCV , or major AI conferences like NeurIPS. Knowledge of computer vision libraries and tools, including OpenCV and DLib, and a solid understanding of image processing and computer vision fundamentals. Hands-on experience with model compression and pruning techniques in deep learning. Good exposure to various deep learning architectures, such as Artificial Neural Networks (ANN), Deep Neural Networks (DNN) , Convolutional Neural Networks (CNN) , Recurrent Neural Networks (RNN ), and Long Short-Term Memory (LSTM) networks. Familiarity with GPU programming (e.g., CUDA, OpenCL) for efficient deep-learning computations. Pay is competitive as per market standards.

Company Description Datakrew is a deep-tech startup based in Singapore, building next-generation analytics for electric vehicle (EV) fleets, batteries, and grid-connected energy assets. Our OXRED platform is deployed across OEMs and operators to extract real-time intelligence from EV battery telemetry, reduce safety risks, and extend usable battery life. We are currently looking for a Battery Intelligence Engineer to support our internal R&D and analytics module development. This role is ideal for someone with a strong interest in battery health modeling, time-series ML, and predictive maintenance. Job Description Responsibilities: Build, train, and benchmark ML models (e.g. LSTM, XGBoost) for battery State-of-Health (SOH), Remaining Useful Life (RUL), and degradation prediction. Analyze large-scale battery performance data to extract actionable insights. Develop anomaly and event detection algorithms for safety-critical battery threats. Create predictive maintenance models to forecast battery failures and degradation. Validate and benchmark model performance using real-world and simulated datasets. Work with the product team to ensure integration of developed models. Stay updated with the latest research in battery modeling and machine learning techniques. Research and collaborate with the team to incorporate electrochemical knowledge into ML models. Qualifications Preferred Skills & Tools: Python (Pandas, NumPy, Scikit-learn, PyTorch or TensorFlow) Strong grasp of time-series modeling, signal processing, and statistical evaluation Familiarity with LSTM, GRU, transformer models, or forecasting pipelines Hands-on experience with deep learning models (CNN, DNN, RNN), NLP techniques, and sequence models Exposure to modern LLM ecosystems (ChatGPT, Gemini, Ollama) and tools like LangChain, AI agents, and prompt engineering Good understanding of battery systems: SOC, SOH, DOD, thermal behaviour, lifecycle stressors Experience working with EV, IoT, or BMS datasets (even in academic projects) SQL basics for querying historical telemetry Nice to Have: Familiarity with Kalman filters, physics-informed models, or hybrid ML-physics methods GitHub portfolio or academic project code repos Knowledge of EV architectures or energy storage standards Additional Information If you’re interested in the EV or battery analytics domain, we welcome your application. Engagement Model: Flexible remote work Duration: 2–4 months (extendable) This is a part-time or freelance position.

Dear Candidate, We are seeking a Machine Learning Engineer to develop predictive models and deploy them into production. Ideal for professionals passionate about AI and data science. Key Responsibilities: Develop and train machine learning models Preprocess and analyze large datasets Deploy models using scalable infrastructure Collaborate with product teams to integrate ML solutions Required Skills & Qualifications: Strong knowledge of Python and ML libraries (scikit-learn, TensorFlow, PyTorch) Experience with data preprocessing and feature engineering Familiarity with model deployment techniques Bonus: Experience with cloud ML services (AWS SageMaker, Google AI Platform) Soft Skills: Strong troubleshooting and problem-solving skills. Ability to work independently and in a team. Excellent communication and documentation skills. Note: If interested, please share your updated resume and preferred time for a discussion. If shortlisted, our HR team will contact you. Kandi Srinivasa Delivery Manager Integra Technologies

Job Description We aim to bring about a new paradigm in medical image diagnostics; providing intelligent, holistic, ethical, explainable and patient centric care. We are looking for innovative problem solvers who love solving problems. We want people who can empathize with the consumer, understand business problems, and design and deliver intelligent products. People who are looking to extend artificial intelligence into unexplored areas. Your primary focus will be in applying deep learning and artificial intelligence techniques to the domain of medical image analysis. Responsibilities Selecting features, building and optimizing classifier engines using deep learning techniques. Understanding the problem and applying the suitable image processing techniques Use techniques from artificial intelligence/deep learning to solve supervised and unsupervised learning problems. Understanding and designing solutions for complex problems related to medical image analysis by using Deep Learning/Object Detection/Image Segmentation. Recommend and implement best practices around the application of statistical modeling. Create, train, test, and deploy various neural networks to solve complex problems. Develop and implement solutions to fit business problems which may include applying algorithms from a standard statistical tool, deep learning or custom algorithm development. Understanding the requirements and designing solutions and architecture in accordance with them. Participate in code reviews, sprint planning, and Agile ceremonies to drive high-quality deliverables. Design and implement scalable data science architectures for training, inference, and deployment pipelines. Ensure code quality, readability, and maintainability by enforcing software engineering best practices within the data science team. Optimize models for production, including quantization, pruning, and latency reduction for real-time inference. Drive the adoption of versioing strategies for models, datasets, and experiments (e.g., using MLFlow, DVC). Contribute to the architectural design of data platforms to support large-scale experimentation and production workloads. Skills and Qualifications Strong software engineering skills in Python (or other languages used in data science) with emphasis on clean code, modularity, and testability. Excellent understanding and hands-on of Deep Learning techniques such as ANN, CNN, RNN, LSTM, Transformers, VAEs etc. Must have experience with Tensorflow or PyTorch framework in building, training, testing, and deploying neural networks. Experience in solving problems in the domain of Computer Vision. Knowledge of data, data augmentation, data curation, and synthetic data generation. Ability to understand the complete problem and design the solutions that best fit all the constraints. Knowledge of the common data science and deep learning libraries and toolkits such as Keras, Pandas, Scikit-learn, Numpy, Scipy, OpenCV etc. Good applied statistical skills, such as distributions, statistical testing, regression, etc. Exposure to Agile/Scrum methodologies and collaborative development practices. Experience with the development of RESTful APIs. The knowledge of libraries like FastAPI and the ability to apply it to deep learning architectures is essential. Excellent analytical and problem-solving skills with a good attitude and keen to adapt to evolving technologies. Experience with medical image analysis will be an advantage. Experience designing and building ML architecture components (e.g., feature stores, model registries, inference servers). Solid understanding of software design patterns, microservices, and cloud-native architectures. Expertise in model optimization techniques (e.g., ONNX conversion, TensorRT, model distillation) Education: BE/B Tech MS/M Tech (will be a bonus) Experience: 3-5 Years

Company: Aiventory (A unit of Jazzoa Services OPC Private Limited) Location: Remote Stipend: ₹3,000 – ₹5,000/month Duration: 3 to 6 months Start Date: Immediate About Us: Aiventory is an AI-powered inventory management platform built specifically for India's Kirana stores and small retail businesses. We're on a mission to bring cutting-edge technology to the underserved sectors of India’s supply chain. We are currently seeking a passionate AI/ML Intern to join our product team and work closely on real-world machine learning applications like demand forecasting, anomaly detection, and computer vision for inventory intelligence. What You'll Work On: Building and training ML models (LSTM, ARIMA, Isolation Forest, etc.) Assisting in real-time prediction engine development for stock trends Image recognition using OpenCV or TensorFlow (barcode/label detection) Data preprocessing and feature engineering for retail datasets Supporting our AI roadmap across backend services You Should Know (at least some of): Python, Pandas, NumPy, Scikit-learn TensorFlow / PyTorch / OpenCV Time Series Forecasting models (ARIMA, LSTM) Basics of SQL or MongoDB Git and version control practices Bonus (Not Mandatory): Experience with retail or inventory datasets Worked on any personal ML projects or GitHub repos Familiarity with cloud (AWS, GCP) or Docker What You’ll Gain: Hands-on experience building real-world AI applications Certificate & Letter of Recommendation Potential for PPO (Pre-Placement Offer) based on performance Opportunity to work directly with the founding team and AI product manager

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title And Summary Data Scientist II-3 Who is Mastercard? Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all. Overview Finicity, a Mastercard company, is leading the Open Banking Initiative to increase the Financial Health of consumers and businesses. The Data Science and Analytics team is looking for a Data Scientist II. The Data Science team works on Intelligent Decisioning; Financial Certainty; Attribute, Feature, and Entity Resolution; Verification Solutions and much more. Join our team to make an impact across all sectors of the economy by consistently innovating and problem-solving. The ideal candidate is passionate about leveraging data to provide high quality customer solutions. Also, the candidate is a strong technical leader who is extremely motivated, intellectually curious, analytical, and possesses an entrepreneurial mindset. Role Manipulates large data sets and applies various technical and statistical analytical techniques (e.g., OLS, multinomial logistic regression, LDA, clustering, segmentation) to draw insights from large datasets. Apply various Machine learning (i.e. SVM, Radom Forest, XGBoost, LightGBM, CATBoost etc), Deep learning techniques (i.e. LSTM, RNN, Transformer etc.) to solve analytical problem statement. Design and implement machine learning models for a number of financial applications including but not limited to: Transaction Classification, Temporal Analysis, Risk modeling from structured and unstructured data. Measure, validate, implement, monitor and improve performance of both internal and external facing machine learning models. Propose creative solutions to existing challenges that are new to the company, the financial industry and to data science. Present technical problems and findings to business leaders internally and to clients succinctly and clearly. Leverage best practices in machine learning and data engineering to develop scalable solutions. Identify areas where resources fall short of needs and provide thoughtful and sustainable solutions to benefit the team Be a strong, confident, and excellent writer and speaker, able to communicate your analysis, vision and roadmap effectively to a wide variety of stakeholders All About You: 3-5 years in data science/ machine learning model development and deployments Exposure to financial transactional structured and unstructured data, transaction classification, risk evaluation and credit risk modeling is a plus. A strong understanding of NLP, Statistical Modeling, Visualization and advanced Data Science techniques/methods. Gain insights from text, including non-language tokens and use the thought process of annotations in text analysis. Solve problems that are new to the company, the financial industry and to data science SQL / Database experience is preferred Experience with Kubernetes, Containers, Docker, REST APIs, Event Streams or other delivery mechanisms. Familiarity with relevant technologies (e.g. Tensorflow, Python, Sklearn, Pandas, etc.). Strong desire to collaborate and ability to come up with creative solutions. Additional Finance and FinTech experience preferred. Bachelor’s or Master’s Degree in Computer Science, Information Technology, Engineering, Mathematics, Statistics. Corporate Security Responsibility Every Person Working For, Or On Behalf Of, Mastercard Is Responsible For Information Security. All Activities Involving Access To Mastercard Assets, Information, And Networks Comes With An Inherent Risk To The Organization And Therefore, It Is Expected That The Successful Candidate For This Position Must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines. #AI Corporate Security Responsibility All Activities Involving Access To Mastercard Assets, Information, And Networks Comes With An Inherent Risk To The Organization And, Therefore, It Is Expected That Every Person Working For, Or On Behalf Of, Mastercard Is Responsible For Information Security And Must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines. R-247687

About The Role We're seeking a skilled and motivated ML/AI Engineer to join our team and drive end-to-end AI development for cutting-edge healthcare prediction models. As part of our AI delivery team you will be working on designing, developing, and deploying ML models to solve complex business challenges. Key Responsibilities Design, develop, and deploy scalable machine learning models and AI solutions. Collaborate with engineers and product managers to understand business requirements and translate them into technical solutions. Analyse and preprocess large datasets for training and testing ML models. Experiment with different ML algorithms and techniques to improve model performance. Technical Skills Proficiency in programming languages such as Python & R Strong knowledge of classical machine learning algorithms with hands on experience in the following : Supervised (Classification model, regressions models) Unsupervised, (Clustering Algorithms, Autoencoders) Ensemble Models (Stacking, Bagging, Boosting techniques, Random Forest, XGBoost) Experience in data preprocessing, feature engineering, and handling large-scale datasets. Model evaluation techniques like accuracy, precision, recall, F1 score, AUC-ROC for classification, and MAE, MSE, RMSE, R-squared for regression. Explainable AI (XAI) techniques include methods like SHAP values, LIME, feature importance from decision trees, and partial dependence plots. Experience with ML frameworks like TensorFlow, PyTorch, Scikit-learn, or Keras. Building & deploying Model APIs using framework like Flask, Fast API, Django, TensorFlow Serving etc. Knowledge of cloud platforms like Azure (preferred), AWS, GCP and experience deploying models in such environments. Nice To Have Object Oriented Programming. Familiarity with NLP, or time series analysis. Exposure to deep learning models (RNN, LSTM) and working with GPUs. (ref:hirist.tech)

Selected Intern's Day-to-day Responsibilities Include Research, identify, and understand business requirements to identify opportunities for applying predictive modeling and machine learning techniques Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to define project objectives and requirements Design and implement predictive modeling and machine learning solutions, including data preprocessing, feature engineering, and model selection Develop and deploy predictive software applications into production, leveraging Python (or similar scripting languages) for efficient development Expertise in training and fine-tuning LLMs on diverse datasets, ensuring superior performance, and adapting models for specific applications and domains. Work with cloud-native solutions on platforms such as AWS, GCP, or Azure for scalability and flexibility Apply a range of machine learning algorithms for different tasks, including classification (SVM, decision tree, random forest, neural network), regression (linear, polynomial, logistic, etc), and graph theory (network analytics) Utilize classical optimization techniques such as gradient descent and others for model optimization and parameter tuning Utilize deep learning techniques, including LSTM, convolutional neural networks (CNN), and recurrent neural networks (RNN), for tasks such as natural language processing, image recognition, or time series analysis Experience with training ML models using Pytorch or tensorflow. About Company: We deliver business growth by providing AI-powered data enrichment. Currently working in stealth mode.

Educational Qualification: Education: Masters or PhD in Computer Science, Machine Learning, AI, Engineering, or a related technical field. Work Experience : Experience: 4-6 years of experience in machine learning based data product research and development in addition to relevant educational background in the field of Computer Science, Engineering, Machine Learning etc. Role Description : We are seeking an AI Scientist to join our analytics team. This role focuses on the development of innovative machine learning and deep learning models that unlock the full potential of Pixxels hyperspectral data. You will work on impactful, data-driven projects aimed at solving complex challenges using state-of-the-art technology and methodologies. As a key contributor to Pixxels analytics vision, you will: Leverage state-of-the-art AI techniques to address complex challenges in imaging analytics, data fusion, and large-scale data processing. Develop multimodal AI models that integrate hyperspectral data with other sensor modalities like radar, LiDAR, and weather datasets, enabling actionable insights for clients across diverse industries. Work closely with cross-functional teams, translating business and scientific objectives into AI-powered solutions that are scalable, efficient, and impactful. Responsibilities & Duties : AI Model Development: Design and implement advanced AI/ML models for classification, prediction, segmentation, and anomaly detection using Pixxels hyperspectral data and multimodal datasets. Multimodal Data Fusion: Integrate diverse data sources, such as hyperspectral, radar, LiDAR, and ancillary datasets, to enhance feature extraction, accuracy, and scalability. Algorithm Optimization: Develop computationally efficient algorithms that can handle large-scale, high-dimensional datasets and deploy them in production environments. R&D Leadership: Research and implement state-of-the-art techniques, including Transformer models, GANs, self-supervised learning, and other emerging techniques, to stay ahead in AI innovation. Cross-Functional Collaboration: Work with domain experts (e.g., hyperspectral scientists, environmental specialists) to understand the data and define project objectives, translating business problems into robust AI models. Communication & Presentation: Clearly explain complex AI concepts, model outputs, and research findings to technical and non-technical stakeholders. Skills and Experience Required: Technical Expertise: Proficiency in advanced machine learning and deep learning architectures (e.g., CNNs, RNNs, Transformers, GANs, LSTMs). Strong background in Python and frameworks such as PyTorch, TensorFlow, or PyTorch Lightning. Experience in developing AI models for complex datasets. Experience with imaging data (e.g., hyperspectral, medical, drone, or satellite imagery), is not required, but would be beneficial. Solid understanding of key principles in multimodal data fusion, computer vision, and large-scale data processing. Communication & Collaboration: Strong communication skills to present findings, teach concepts, and collaborate effectively with non-AI experts. Ability to work cross-functionally with data scientists, engineers, and domain specialists . R&D Mindset: Proven experience in implementing cutting-edge research techniques and staying up-to-date with advancements in AI. Candidate Acumen : As an AI Scientist, I bring expertise in developing advanced machine learning and deep learning models to extract insights from hyperspectral data, leveraging state-of-the-art methodologies to tackle complex, data-driven challenges. Benefits: Comprehensive insurance coverage for you and your family. Free access to mental health professionals and paid sick leave. Benefits like vaccination drives and more. Flexible working hours. Opportunity to work with top scientists and professionals in space technology.

Job Description: We're seeking a skilled and motivated ML/AI Engineer to join our team and drive end-to-end AI development for cutting-edge healthcare prediction models. As part of our AI delivery team you will be working on designing, developing, and deploying ML models to solve complex business challenges. Key Responsibilities: Design, develop, and deploy scalable machine learning models and AI solutions. Collaborate with engineers and product managers to understand business requirements and translate them into technical solutions. Analyse and preprocess large datasets for training and testing ML models. Experiment with different ML algorithms and techniques to improve model performance. Skills & Qualifications: Experience: B.Tech with 3-6 years of relevant experience in Machine Learning, AI, or Data Science roles. OR M.Tech, M. Statistics with 2-4 years of relevant experience in Machine Learning, AI, or Data Science roles. Education: Bachelor's/master's degree in computer science or Statistics or any other relevant engineering / AI course from a Tier 2 college or similar colleges/universities. Technical Skills: Proficiency in programming languages such as Python & R Strong knowledge of classical machine learning algorithms with hands on experience in the following: Supervised (Classification model, regressions models) Unsupervised, (Clustering Algorithms, Autoencoders) Ensemble Models (Stacking, Bagging, Boosting techniques, Random Forest, XGBoost) Experience in data preprocessing, feature engineering, and handling large-scale datasets. Model evaluation techniques like accuracy, precision, recall, F1 score, AUC-ROC for classification, and MAE, MSE, RMSE, R-squared for regression. Explainable AI (XAI) techniques include methods like SHAP values, LIME, feature importance from decision trees, and partial dependence plots. Experience with ML frameworks like TensorFlow, PyTorch, Scikit-learn, or Keras. Building & deploying Model APIs using framework like Flask, Fast API, Django, TensorFlow Serving etc. Knowledge of cloud platforms like Azure (preferred), AWS, GCP and experience deploying models in such environments -> changed the wording, added points. Nice to Have: Object Oriented Programming. Familiarity with NLP, or time series analysis. -> Moving this into Nice to have, not bare min Exposure to deep learning models (RNN, LSTM) and working with GPUs. Show more Show less

Title : Data Scientist Experience : 4 to 6 years Company : Scry AI Skill Sets : GEN AI (LLM), Python, Scikit-learn, SQL, k-means, decision trees, random forests, neural networks, genetic algorithms About The Role Design and develop Predictive Analytics and Prescriptive Analytics architecture and features for artificial intelligence software solutions, including developing machine learning and statistical models to perform big data analysis. Duties Will Include The Following Research, design and develop algorithms and techniques to perform data manipulation and transformation; build generative and discriminative models to perform clustering, classification, regression, and recommendation systems. Design and build custom machine learning models. Building GEN AI (LLM) pipelines using LangChain, LlamaIndex, Vector DBs, embedding generation and Hugging Face. Build and implement machine learning algorithms to plan, create, coordinate, and deploy business information solutions; gather business requirements and technical specifications for developing analytics solutions. Develop artificial intelligence and machine learning algorithms. Custom design and implement various techniques in machine learning such as support vector machines, neural networks, genetic algorithms, principal component analysis, cluster analysis, k-means, decision trees, random forests, regression analysis, Bayesian analysis, Na- ve Bayes, and deep learning. Analyze large, complex and multi-dimensional data sets. Develop analytic solutions using R, Python, Scikit-learn, SQL and similar statistical tools and frameworks. Utilize Deep Learning neural networks including RNN, CNN, LSTM, and Autoencoder to predict the behavior and performance of businesses. Requirements Masters degree (or equivalent-) in Data Informatics, Computer Science, Applied Mathematics, Statistics, Analytics or a related field such as Computer Science and Engineering. Minimum 4+ years of relevant experience in the job offered or as a Software Engineer, Software Engineering Analyst, Application Developer, Programmer Analyst. Minimum 2 years of experience with algorithm design and development; software architecture development; data engineering, data structures, and data visualization; statistical data analysis. Artificial Intelligence (AI) and deep learning techniques; feature synthesis, engineering, and hyper-parameter tuning. Natural Language Processing; Machine Learning, including Bayesian modeling, multivariate and logistic regression, support vector machines, cluster analysis, decision and regression trees, random forest, neural networks and ensemble methods; analyzing large, complex, multi-dimensional data sets and developing analytic solutions. Experience working with structured and unstructured data (text, image). Working experience with unstructured data such as audios and videos is not mandatory but will be an add-on advantage. Experience in predictive analytics using R, Python, Scikit-learn, and SQL, PostgreSQL statistical tools; Spark, Hadoop, Unix O/S, GitHub; and programming languages Java, Python, R. Our Perfect Candidate Is Someone That Is proactive and an independent problem solver. Is a constant learner. We are a fast-growing company. We want you to grow with us! Is a team player and good communicator. (ref:hirist.tech) Show more Show less

As Lead ML Engineer , you'll lead the development of predictive models for demand forecasting, customer segmentation, and retail optimization, from feature engineering through deployment. As Lead ML Engineer, you'll lead the development of predictive models for demand forecasting, customer segmentation, and retail optimization, from feature engineering through deployment. Responsibilities: Build and deploy models for forecasting and optimization Perform time-series analysis, classification, and regression Monitor model performance and integrate feedback loops Use AWS SageMaker, MLflow, and explainability tools (e.g., SHAP or LIME)

Job Description : Should have 4+ years hands-on experience in algorithms and implementation of analytics solutions in predictive analytics, text analytics and image analytics Should have handson experience in leading a team of data scientists, works closely with client’s technical team to plan, develop and execute on client requirements providing technical expertise and project leadership. Leads efforts to foster innovative ideas for developing high impact solutions. Evaluates and leads broad range of forward looking analytics initiatives, track emerging data science trends, and knowledge sharing Engaging key stakeholders to source, mine and validate data and findings and to confirm business logic and assumptions in order to draw conclusions. Helps in design and develop advanced analytic solutions across functional areas as per requirement/opportunities. Technical Role and Responsibilities Demonstrated strong capability in statistical/Mathematical modelling or Machine Learning or Artificial Intelligence Demonstrated skills in programming for implementation and deployment of algorithms preferably in Statistical/ML based programming languages in Python Sound Experience with traditional as well as modern statistical techniques, including Regression, Support Vector Machines, Regularization, Boosting, Random Forests, and other Ensemble Methods; Visualization tool experience - preferably with Tableau or Power BI Sound knowledge of ETL practices preferably spark in Data Bricks cloud big data technologies like AWS, Google, Microsoft, or Cloudera. Communicate complex quantitative analysis in a lucid, precise, clear and actionable insight. Developing new practices and methodologies using statistical methods, machine learning and predictive models under mentorship. Carrying out statistical and mathematical modelling, solving complex business problems and delivering innovative solutions using state of the art tools and cutting-edge technologies for big data & beyond. Preferred to have Bachelors/Masters in Statistics/Machine Learning/Data Science/Analytics Should be a Data Science Professional with a knack for solving problems using cutting-edge ML/DL techniques and implementing solutions leveraging cloud-based infrastructure. Should be strong in GCP, TensorFlow, Numpy, Pandas, Python, Auto ML, Big Query, Machine learning, Artificial intelligence, Deep Learning Exposure to below skills: Preferred Tech Skills : Python, Computer Vision,Machine Learning,RNN,Data Visualization,Natural Language Processing,Voice Modulation,Speech to text,Spicy,Lstm,Object Detection,Sklearn,Numpy, NLTk,Matplotlib,Cuinks, seaborn,Imageprocessing, NeuralNetwork,Yolo, DarkFlow,DarkNet,Pytorch, CNN,Tensorflow,Keras,Unet, ImageSegmentation,ModeNet OCR,OpenCV,Pandas,Scrapy, BeautifulSoup,LabelImg ,GIT. Machine Learning, Deep Learning, Computer Vision, Natural Language Processing,Statistics Programming Languages-Python Libraries & Software Packages- Tensorflow, Keras, OpenCV, Pillow, Scikit-Learn, Flask, Numpy, Pandas, Matplotlib,Docker Cloud Services- Compute Engine, GCP AI Platform, Cloud Storage, GCP AI & MLAPIs Job Types: Full-time, Permanent, Fresher Pay: ₹30,000.00 - ₹80,000.00 per month Education: Bachelor's (Preferred) Experience: Machine learning: 4 years (Preferred) Work Location: In person

Role Summary As a Data Science Lead at Worley, you will collaborate with an established team to deliver high-impact data science and AI/ML projects for clients, while continuing to expand your own technical expertise. The role focuses particularly on working with unstructured data and involves designing scalable AI/ML solutions, staying on the leading edge of technology, and driving innovation across both internal and external stakeholders. Key Responsibilities Platform & Tool Development: Conceptualize, build, and manage AI/ML platforms with a focus on unstructured data. Evaluate and select the best AI/ML tools and frameworks in the industry. Leverage existing frameworks, standards, and patterns to create scalable architectural foundations. Solution Delivery & Ownership: Lead the development of cognitive solutions for both internal stakeholders and external customers. Conduct research in areas like Explainable AI, image segmentation, 3D object detection, and statistical methods. Ensure delivery of scalable, enterprise-grade applications and services. Strategic Insight & Innovation: Analyse global market trends (economic, social, cultural, technological) to identify AI/ML opportunities. Shape value propositions and recommendations using a global perspective. Evaluate algorithms, models, and emerging technologies to optimize organizational investment. Collaboration & Leadership: Guide and support data scientists and engineers in project execution involving large and complex datasets. Establish and oversee AI/ML lifecycle processes and best practices. Ensure collaboration across multi-disciplinary teams. Technical Skills & Experience AI/ML Expertise: Deep understanding of the full AI/ML project life cycle. Expert in deep learning and reinforcement learning. Experience with MLOps practices for effective model lifecycle management. Proficient in machine learning algorithms: k-NN, GBM, Naive Bayes, SVM, Neural Networks, Decision Forests. Tools & Frameworks: Hands-on experience with tools such as: Jupyter Hub, Zeppelin Notebook, Azure ML Studio TensorFlow, TensorFlow-Keras, PyTorch, SciKit-Learn, Spark MLlib Strong knowledge of CNN, R-CNN, LSTM, Encoder/Transformer architectures. Experience with deep learning models like Inception-ResNet and ResNeXt-50. Proven use of RNNs for text, speech, and generative models. Data Engineering & Storage: Familiarity with NoSQL databases (GraphX, Neo4J), document, columnar, and in-memory data models. Experience with ETL tools and platforms such as Talend, SAP BI Platform, SSIS, and MapReduce. Visualization & Reporting: Skilled in building KPI dashboards and storytelling visuals using tools like Tableau or Zoomdata. Preferred Qualifications Advanced degree in Data Science, Computer Science, AI/ML, or related fields. Demonstrated leadership in managing AI/ML projects and teams. Prior experience in consulting or energy/resources industries is a plus.

Role Summary As a Data Science Lead at Worley, you will collaborate with an established team to deliver high-impact data science and AI/ML projects for clients, while continuing to expand your own technical expertise. The role focuses particularly on working with unstructured data and involves designing scalable AI/ML solutions, staying on the leading edge of technology, and driving innovation across both internal and external stakeholders. Key Responsibilities Platform & Tool Development: Conceptualize, build, and manage AI/ML platforms with a focus on unstructured data. Evaluate and select the best AI/ML tools and frameworks in the industry. Leverage existing frameworks, standards, and patterns to create scalable architectural foundations. Solution Delivery & Ownership: Lead the development of cognitive solutions for both internal stakeholders and external customers. Conduct research in areas like Explainable AI, image segmentation, 3D object detection, and statistical methods. Ensure delivery of scalable, enterprise-grade applications and services. Strategic Insight & Innovation: Analyse global market trends (economic, social, cultural, technological) to identify AI/ML opportunities. Shape value propositions and recommendations using a global perspective. Evaluate algorithms, models, and emerging technologies to optimize organizational investment. Collaboration & Leadership: Guide and support data scientists and engineers in project execution involving large and complex datasets. Establish and oversee AI/ML lifecycle processes and best practices. Ensure collaboration across multi-disciplinary teams. Technical Skills & Experience AI/ML Expertise: Deep understanding of the full AI/ML project life cycle. Expert in deep learning and reinforcement learning. Experience with MLOps practices for effective model lifecycle management. Proficient in machine learning algorithms: k-NN, GBM, Naive Bayes, SVM, Neural Networks, Decision Forests. Tools & Frameworks: Hands-on experience with tools such as: Jupyter Hub, Zeppelin Notebook, Azure ML Studio TensorFlow, TensorFlow-Keras, PyTorch, SciKit-Learn, Spark MLlib Strong knowledge of CNN, R-CNN, LSTM, Encoder/Transformer architectures. Experience with deep learning models like Inception-ResNet and ResNeXt-50. Proven use of RNNs for text, speech, and generative models. Data Engineering & Storage: Familiarity with NoSQL databases (GraphX, Neo4J), document, columnar, and in-memory data models. Experience with ETL tools and platforms such as Talend, SAP BI Platform, SSIS, and MapReduce. Visualization & Reporting: Skilled in building KPI dashboards and storytelling visuals using tools like Tableau or Zoomdata. Preferred Qualifications Advanced degree in Data Science, Computer Science, AI/ML, or related fields. Demonstrated leadership in managing AI/ML projects and teams. Prior experience in consulting or energy/resources industries is a plus.

Role Summary As a Data Science Lead at Worley, you will collaborate with an established team to deliver high-impact data science and AI/ML projects for clients, while continuing to expand your own technical expertise. The role focuses particularly on working with unstructured data and involves designing scalable AI/ML solutions, staying on the leading edge of technology, and driving innovation across both internal and external stakeholders. Key Responsibilities Platform & Tool Development: Conceptualize, build, and manage AI/ML platforms with a focus on unstructured data. Evaluate and select the best AI/ML tools and frameworks in the industry. Leverage existing frameworks, standards, and patterns to create scalable architectural foundations. Solution Delivery & Ownership: Lead the development of cognitive solutions for both internal stakeholders and external customers. Conduct research in areas like Explainable AI, image segmentation, 3D object detection, and statistical methods. Ensure delivery of scalable, enterprise-grade applications and services. Strategic Insight & Innovation: Analyse global market trends (economic, social, cultural, technological) to identify AI/ML opportunities. Shape value propositions and recommendations using a global perspective. Evaluate algorithms, models, and emerging technologies to optimize organizational investment. Collaboration & Leadership: Guide and support data scientists and engineers in project execution involving large and complex datasets. Establish and oversee AI/ML lifecycle processes and best practices. Ensure collaboration across multi-disciplinary teams. Technical Skills & Experience AI/ML Expertise: Deep understanding of the full AI/ML project life cycle. Expert in deep learning and reinforcement learning. Experience with MLOps practices for effective model lifecycle management. Proficient in machine learning algorithms: k-NN, GBM, Naive Bayes, SVM, Neural Networks, Decision Forests. Tools & Frameworks: Hands-on experience with tools such as: Jupyter Hub, Zeppelin Notebook, Azure ML Studio TensorFlow, TensorFlow-Keras, PyTorch, SciKit-Learn, Spark MLlib Strong knowledge of CNN, R-CNN, LSTM, Encoder/Transformer architectures. Experience with deep learning models like Inception-ResNet and ResNeXt-50. Proven use of RNNs for text, speech, and generative models. Data Engineering & Storage: Familiarity with NoSQL databases (GraphX, Neo4J), document, columnar, and in-memory data models. Experience with ETL tools and platforms such as Talend, SAP BI Platform, SSIS, and MapReduce. Visualization & Reporting: Skilled in building KPI dashboards and storytelling visuals using tools like Tableau or Zoomdata. Preferred Qualifications Advanced degree in Data Science, Computer Science, AI/ML, or related fields. Demonstrated leadership in managing AI/ML projects and teams. Prior experience in consulting or energy/resources industries is a plus.

MAJOR ACCOUNTABILITIES OF POSITION: Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress Utilize the existing frameworks, standards, patterns to create architectural foundation and services necessary for AI applications that scale from multi-user to enterprise class Managing Data Science project life cycle from exploratory data analysis to productization (Alpha/Beta Release) .Manage small team to collaborate with Architecture, Data Warehouse, Data Governance teams for providing analytics as service. Mentor team member for AI/ML development Verifying data quality, and/or ensuring it via data cleaning Supervising the data acquisition process if more data is needed Finding available datasets online that could be used for training Defining validation strategies Defining the preprocessing or feature engineering to be done on a given dataset. Defining data augmentation pipelines Training models and tuning their hyperparameters Analyzing the errors of the model and designing strategies to overcome them Deploying models to production. Development of the ML algorithms that could be used to solve a given problem and ranking them by their success probability. Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world Knowledge / Experience / Competencies Required IT Skills & Experience (Priority wise) 1. Proven experience as a Data Scientist AI/ML or similar role 2. Ability to write robust code in Python. 3. Experience in the Generative AI components like LLMs, LangChain, LlamaIndex, OpenAI, Mistral, Llama etc.

About Us Paxcom a leading Digital Solution Provider is a part of Paymentus now, a leading electronic bill payment provider. PaymentUs leads the North American marketplace in electronic bill payment solutions and have recently signed a partnership with Paypal and Alexa. Recognized by Deloitte as one of the fastest growing companies in North America, Paymentus is the premier provider of innovative, reliable, and secure electronic bill presentment and payment services for more than 1300 clients leading the Utility, Telecom, Auto Finance, Insurance, Consumer Finance, and Health industries. Our comprehensive eBilling and Payment Platform allows our clients to provide a unified customer bill-pay experience that includes online, mobile, IVR, text, kiosk, and agent-assisted channels, as well as a full range of customer communication options. For more details, please visit www.paymentus.com Job Location: Gurugram Job Type: Permanent Interview process: Coding challenge >> Round 1 (Technical Webcam>> Managerial webcam round (Tech + Managerial) Join our team as an AI/ML Specialist and contribute to the delivery of cutting-edge capabilities by leveraging the latest technologies. In this role, you will collaborate closely with stakeholders and technical team members to drive impactful solutions. Requirements Minimum of 4 years of relevant experience in Data Science, Machine Learning, and AI techniques, with a strong background in open source technologies. Exposure to Gen AI models such as Falcon, Llama 2, GPT 3.5 & 4, and Prompt Engineering. Experience in creating basic RAG pipelines. Familiarity with cloud services such as AWS (EC2, S3, ECR). Expertise in at least one of the following areas: Time Series Analysis, Standard Machine Learning Algorithms, or Deep Learning. Experience with statistical analysis, data mining, temporal and pattern analysis, correlation of events, predictive modeling, and pattern recognition for various use cases. Area Of Expertise In-depth knowledge and hands-on experience with at least one object detection framework including TFOD, Detectron, and YOLO, operating within a large-scale distributed platform. Or, Solid understanding of Deep Learning fundamentals (CNN, RNN, attention/memory) and extensions (Transformer, LSTM, ResNet, etc.). Or, Knowledge of state-of-the-art ML algorithms such as BERT, ELMo, GPT, GPT-2, XLNET, T5, LSTMs, CRFs, etc., APIs, ONNX, and open-source methods. Strong experience with data science tools including Python scripting, CUDA, numpy, scipy, matplotlib, scikit-learn, bash scripting, and Linux environment. on-making and the ability to justify actions effectively. Why Join Us? Empowerment to focus on pivotal tasks. Embrace a flexible and laid-back work atmosphere. Prioritize work-life balance. Appreciate collaborating with a goal-driven team. Interact with an approachable, supportive, and accomplished management team. Competitive compensation package. Engage with cutting-edge technologies as they emerge in the market. Witness the direct impact of your code on the lives of millions of customer. Show more Show less

Skills Skill SDLC PL/SQL Solution Architecture Java Unix .NET SOA Microsoft SQL Server SQL ASP.NET Education Qualification No data available CERTIFICATION No data available Role Data Scientist Educational Qualification ME/BE / MCA Experience Required 4+ Years Shifts Day shift Skills & Responsibilities Experience4+ years in machine learning, deep learning, or generative AI. Programming & Frameworks Strong Python scripting with Pandas, NumPy, OOPs concepts. Experience with PyTorch, TensorFlow, Keras, Hugging Face Transformers. Proficiency in SQL queries when needed. Generative AI & NLP Experience with LLMs, GANs, VAEs, Diffusion Models. Familiarity with OpenAI GPT, DALLE, Stable Diffusion. Deep knowledge of NLP techniques & deep learning architectures (RNN, CNN, LSTM, GRU). Machine Learning & Statistics Understanding of ML/DL algorithms, statistical analysis, and feature engineering. Theoretical knowledge of Random Forest, SVM, Boosting, Bagging, Regression (Linear & Logistic), and Unsupervised Learning. MLOps & Deployment Familiarity with Cloud platforms (AWS, Azure, GCP). Experience in MLOps, CI/CD, Docker, Kubernetes. Comfortable with Linux systems and GPU-based deep learning. Research & Ethics Contributions to AI research, open-source projects, or Kaggle competitions. Awareness of AI ethics, bias mitigation, and model interpretability. Soft Skills & Work Environment Ability to work independently and deliver results. Experience in an agile development environment. Knowledge of computer vision is a plus.