221 Model Development Jobs - Page 5

Job Title Ind & Func AI Decision Science Analyst - S&C GN Management Level :11 - Analyst Location:Gurgaon Must have skills: Generative AI, Machine Learning, Large Language Models (LLMs), Python, SQL Good to have skills:Spark, Cloud Platforms (AWS, Azure, GCP), NLP, Computer Vision Job Summary : As an AI Decision Science Analyst, you will play a key role in designing, building, and deploying advanced AI models and solutions to address business challenges across various industries. You will leverage Generative AI, Machine Learning, and Large Language Models (LLMs) to drive innovation and deliver impactful insights for clients. Your work will involve collaborating with cross-functional teams, and contributing to the development of advanced analytics capabilities. Roles & Responsibilities: Leverage Advanced Data Science Techniques Develop solutions using Generative AI, Machine Learning, and Large Language Models (LLMs). Define data requirements, clean, aggregate, analyze, and interpret data while conducting data quality assessments. Develop and Implement AI Models Build and deploy AI models and Generative AI applications. Train and fine-tune LLMs using large-scale datasets to optimize performance and accuracy. Evaluate model performance and implement iterative improvements. Solution Integration and Deployment Collaborate to integrate AI solutions into end-to-end workflows, ensuring scalability. Utilize cloud platforms like AWS, Azure, or GCP for model development and deployment. Innovation and Knowledge Sharing Stay updated on advancements in AI and Data Science, exploring innovative techniques and frameworks. Document methodologies and findings for effective knowledge sharing. Professional & Technical Skills: - Must To Have Skills: Proficiency in Generative AI, Machine Learning, LLMs, Python, SQL. Spark, Cloud platforms (AWS, Azure, GCP), NLP, Computer Vision. Experience in developing and AI/ML models. Additional Information: - The ideal candidate will possess a strong educational background in computer science or a related field, along with a proven track record of delivering impactful solutions using data science and analytics. This position is based at our Gurugram office. About Our Company | Accenture Qualification Experience: Minimum 1+ years of experience in Data Science, preferably within a consulting environment Educational Qualification: Bachelors or Masters degree (BE/BTech/MBA) in Statistics, Computer Science, Mathematics, or related disciplines with an excellent academic record

Management Level :07- I&F Decision Sci Practitioner Manager Location :Mumbai Must-have skills :Risk Analytics, Model Development, Validation, and Auditing, Performance Evaluation, Monitoring, Governance, Statistical Techniques:Linear Regression, Logistic Regression, GLM, GBM, XGBoost, CatBoost, Neural Networks, Programming Languages:SAS, R, Python, Spark, Scala, Tools:Tableau, QlikView, PowerBI, SAS VA, Regulatory Knowledge:Basel/CCAR/DFAST/CECL/IFRS9, Risk Reporting and Dashboard Solutions Good to have skills :Advanced Data Science Techniques, AML, Operational Risk Modelling, Cloud Platform Experience (AWS/Azure/GCP), Machine Learning Interpretability and Bias Algorithms Job Summary We are seeking a highly skilled I&F Decision Sci Practitioner Manager to join the Accenture Strategy & Consulting team in the Global Network Data & AI practice. You will be responsible for leading risk model development, validation, and auditing activities, ensuring performance evaluation, monitoring, governance, and documentation. This role also provides opportunities to work with top financial clients globally, utilizing cutting-edge technologies to drive business capabilities and foster innovation. Roles & Responsibilities: Engagement Execution Lead the team in the development, validation, governance, strategy, transformation, implementation, and end-to-end delivery of risk solutions for clients. Manage workstreams for large and small projects, overseeing the quality of deliverables for junior team members. Develop and frame Proof of Concept for key clients where applicable. Practice Enablement Mentor, guide, and counsel analysts and consultants. Support the development of the practice by driving innovations and initiatives. Support efforts of sales team to identify and win potential opportunities by assisting with RFPs, RFI. Assist in designing POVs, GTM collateral. Professional & Technical Skills: 7-12 years of relevant Risk Analytics experience at one or more Financial Services firms or Professional Services / Risk Advisory with significant exposure to Credit Risk :PD/LGD/EAD Models, CCAR/DFAST Loss Forecasting, Revenue Forecasting Models, IFRS9/CECL Loss Forecasting across Retail and Commercial portfolios. Credit Acquisition/Behavior :Modeling, Credit Policies, Limit Management, Acquisition Frauds, Collections Agent Matching/Channel Allocations across Retail and Commercial portfolios. Regulatory Capital and Economic Capital Models Liquidity Risk :Liquidity Models, Stress Testing Models, Basel Liquidity Reporting Standards Anti-Money Laundering (AML) :AML Scenarios/Alerts, Network Analysis Operational Risk :AMA Modeling, Operational Risk Reporting Modeling Techniques :Linear Regression, Logistic Regression, GLM, GBM, XGBoost, CatBoost, Neural Networks, Time Series (ARMA/ARIMA), ML Interpretability and Bias Algorithms Programming Languages & Tools :SAS, R, Python, Spark, Scala, Tableau, QlikView, PowerBI, SAS VA Strong understanding of Risk functions and their application in client discussions and project implementation. Additional Information: Masters Degree in a quantitative discipline (mathematics, statistics, economics, financial engineering, operations research) or MBA from top-tier universities Industry Certifications :FRM, PRM, CFA preferred Excellent Communication and Interpersonal Skills About Our Company | Accenture Qualification Experience :Minimum 7-12 years of relevant Risk Analytics experience, Exposure to Financial Services firms or Professional Services/Risk Advisory Educational Qualification :Masters degree in a quantitative discipline (mathematics, statistics, economics, financial engineering, operations research) or MBA from top-tier universities, Industry certifications such as FRM, PRM, CFA preferred

Job Summary: Job Function: Model Development Associate Consultant/Consultant, is a key member of the Risk Analytics and Data Service Team and Responsible for acting as an individual contributor in the development and maintenance of high quality risk analytics. Resolves complex issues in Credit Risk/ PPNR modelling and measuring risk, enhancement in Credit Risk/ PPNR methodologies or other aspects of risk measurement. Job title: Associate Consultant/Consultant Location: Bangalore Experience: 4-8 years of relevant experience Major Duties Responsible for Development of CCAR models (PD/EAD/LGD) , CECL models (PD/EAD/LGD) and Basel models (PD/EAD/LGD). Also for PPNR model development for Non-Interest Income, Net Interest Income, Expense, Deposit, Balance models. Ensures regular production of analytical work.. Collaborates with regulators, Audit Services, and other independent reviewers. Evaluates existing framework in relation to corporate objectives and industry leading practices. Assesses development needs and manages process to achieve desired future state. Supports stress testing, capital quantification and/or internal capital allocation methodologies. Ensures that modelling approaches meet both internal corporate needs and regulatory requirements related to prevailing regulatory guidance. Provides technical/theoretical inputs to resolve risk issues and enhance overall risk framework. Works with other risk or business unit teams to ensure that risk management policies/processes and quantitative modelling approaches are consistent. Operates independently; has knowledge of banking balance sheets and income statements. Conducts analysis, independently ensuring accuracy and completeness. Responsible for interaction with different committees and/or senior management. Qualification: Master in Statistics/ Economics/Mathematics/advanced degree in quant area Or B.tech. From tier 1 college with MBA in related field Skills Required: Strong BASEL, CCAR and DFAST, SR-11/7 understanding. Strong regulatory understanding 2+ years of hands on model building experience in Credit Risk / PPNR Strong conceptual and technical knowledge of risk concepts and quantitative modelling techniques including familiarity with statistical concepts used in stress testing Strong in quantitative skills - experience in model validation a plus Experience in R, SAS, Matlab, advanced Excel techniques and VBA programming. SAS is preferred Strong Experience in building linear regression models, Nonlinear regression, time series modeling (ARIMA, AR, VAR, MA ) and stochastic process Strong organizational and interpersonal skills Excellent verbal and written communication skills (English) Experience of working in a multi-cultural and global environment Related Industry qualification (e.g., CFA, FRM) a plus

Job Summary This job will oversee the strategic direction and execution of machine learning projects. You will work closely with data scientists, software engineers, and product teams to enhance services through innovative AI/ML solutions. Your role will involve building scalable ML pipelines, ensuring data quality, and deploying models into production environments to drive business insights and improve customer experiences. Your Way to Impact As a Sr. Staff Machine Learning Engineer, you ll be instrumental in scaling PayPal s AI capabilities through advanced fine-tuning and foundational model development. Your work will directly enhance how we serve our customers with intelligent, adaptive, and personalized experiences. From conversational agents to smart automation, the models you build will be embedded across PayPal s ecosystem, impacting millions of users globally. Meet Our Team You ll be part of the Applied Intelligence organization, working alongside product, platform, and infrastructure teams. We power next-gen experiences through a unified AI stack accelerating time-to-value for internal users and building intuitive solutions for consumers and merchants. You ll be joining a team that values experimentation, scalability, and operational excellence in AI systems. Job Description Your Day to Day Fine-tune and optimize advanced machine learning and foundational models. Build reusable training pipelines and inference systems for production-grade deployment. Collaborate with cross-functional teams to integrate models into product experiences and backend workflows. Continuously monitor, test, and improve model performance in live environments. Mentor junior engineers and contribute to team-wide knowledge sharing. Participate in internal forums, technical design reviews, and possibly external publications. What You Need to Bring Bachelor s degree or equivalent, with at least 12-15 years of relevant experience. Strong hands-on experience with ML frameworks like PyTorch, TensorFlow, or Hugging Face. Experience fine-tuning large models (e.g., LLMs, vision-language models) using SFT, LoRA, RLHF, etc. Proven experience deploying ML models on cloud platforms such as AWS, GCP, or Azure. Deep knowledge of MLOps practices and tooling (e.g., model registries, CI/CD for ML). Preferred Qualification Subsidiary PayPal Travel Percent 0 For the majority of employees, PayPals balanced hybrid work model offers 3 days in the office for effective in-person collaboration and 2 days at your choice of either the PayPal office or your home workspace, ensuring that you equally have the benefits and conveniences of both locations. Our Benefits We have great benefits including a flexible work environment, employee shares options, health and life insurance and more. To learn more about our benefits please visit https//www.paypalbenefits.com . Who We Are Click Here to learn more about our culture and community. Commitment to Diversity and Inclusion PayPal provides equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, pregnancy, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state, or local law. In addition, PayPal will provide reasonable accommodations for qualified individuals with disabilities. . Belonging at PayPal Our employees are central to advancing our mission, and we strive to create an environment where everyone can do their best work with a sense of purpose and belonging. Belonging at PayPal means creating a workplace with a sense of acceptance and security where all employees feel included and valued. We are proud to have a diverse workforce reflective of the merchants, consumers, and communities that we serve, and we continue to take tangible actions to cultivate inclusivity and belonging at PayPal. Any general requests for consideration of your skills, please Join our Talent Community . We know the confidence gap and imposter syndrome can get in the way of meeting spectacular candidates. Please don t hesitate to apply.

Responsibilities: * Develop machine learning models using TensorFlow, NumPy & OpenCV. * Implement computer vision solutions with CNNs & object detection techniques. Provident fund

Job Summary Should have worked hands-on in setting up ML services on public cloud and delivering ML based solutions. Should have 7 to 10 years of experience in AI ML and at least 1 year in GenAI leveraging cloud based GenAI services. Should have through knowledge of end-to-end deployment of GenAI solutions to customers cutting across the industry prevalence of GenAI. Responsibilities Key Responsibilities Research and Development Stay updated on generative AI advancements and methodologies conducting research to explore new techniques and experimenting with cutting-edge frameworks and libraries. Architecture Design Design tailored generative AI systems for specific applications developing scalable architectures leveraging cloud infrastructure and balancing model complexity with practical deployment considerations. Model Development and Training Develop custom generative models using deep learning frameworks training models on large-scale datasets with advanced optimization techniques and fine-tuning parameters to optimize performance metrics. Performance Evaluation and Optimization Evaluate model performance using quantitative and qualitative metrics identifying areas for improvement and optimizing model architectures to ensure efficiency and effectiveness of generative AI solutions. Integration and Deployment Integrate generative AI models into existing systems and applications developing APIs and interfaces for seamless interaction with other components and ensuring compatibility scalability and reliability in production environments. Collaboration and Communication Collaborate with cross-functional teams to understand requirements effectively communicate technical concepts to stakeholders and provide guidance and mentorship to junior team members. Technical Expertise Should have worked hands on in setting up ML services on public cloud and delivering ML based solutions. Should have 3 to 5 years of experience in AI ML and at least 1 year in GenAI leveraging cloud based GenAI services. Should have through knowledge of end-to-end deployment of GenAI solutions to customers cutting across the industry prevalence of GenAI. Should have handson experience in prompt engineering maintaining prompt libraries managing GenAI delivered code filtering avoiding duplicates etc. Should have created large scale PoVs or PoCs and presented to senior client teams. Should be able to define GenAI adoption roadmap for client industries based on their Enterprise needs. #LI-LK1

This role is to solve business problems in Machine Learning for the Seller and Fulfilment Tech (SFT) org. The overarching goal of the team is to enhance ML expertise and fluency within SFT and across IST, championing engineering and operational excellence in ML model development and other related parts of the ML model lifecycle. Some of the key areas which the team owns in this space area: Selection Recommendations, Registration improvements, Bad actor detection and prevention Selection economics, Inventory recommendation, Delivery Promise Predictions, Seller success. Within the ML space, the scientist would have to solve intrinsically hard problems where neither problem nor solution is well defined. So, the leader should have high focus on building a deep understanding of the ML science space, experimentation methodology, as well as a high focus on embracing external trends, especially applications of GenerativeAI and LLMs. A large focus area for the role is to also contribute towards the science and research aspects. The ASII leader applies and extends existing scientific techniques, and invents new ones to address specific customers needs or business problems, at a project level. This should also lead to regular contributions to internal or external peer-reviewed publications that validate novelty 3+ years of building models for business application experience PhD, or Masters degree and 4+ years of CS, CE, ML or related field experience Experience in patents or publications at top-tier peer-reviewed conferences or journals Experience programming in Java, C++, Python or related language Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing Experience using Unix/Linux Experience in professional software development

Job_Description":" Job Title: Data Science CoE Location: Hyderabad, India (Hybrid) Experience: 6+ years Role Type: Full-time Start Date : Immediate About the Role: As we build our Data Science Center of Excellence (CoE), we are looking for an entrepreneurial and technically strong Data Science Lead who can lay the foundation for a high-performing team. You will work directly with stakeholders across multiple business units to define use cases, lead model development, and ensure successful deployment and value realization. Key Responsibilities: Serve as the technical and strategic lead for the Data Science CoE. Partner with business leaders to identify use cases, assess feasibility, and define measurable outcomes. Design end-to-end data science solutions \u2014 from problem framing and data exploration to model development and deployment. Build and mentor a team of data scientists as the CoE scales. Promote best practices in experimentation, model development, versioning, documentation, and governance. Collaborate closely with engineering and data platform teams for scalable solution delivery. Evangelize data science best practices and build credibility through delivery of impactful projects. Qualifications: 10+ years of experience in applied data science, machine learning, or AI solutioning. Strong portfolio of business-impacting projects across domains such as customer analytics, personalization, marketing, pricing, operations, or product. Experience working with cloud platforms (AWS, Azure, or GCP) and modern data science tools (Python, R, SQL, etc.). Strong stakeholder management and communication skills to explain complex models in simple business terms. Prior experience in setting up data science teams, mentoring junior talent, and working in a cross-functional delivery environment. Exposure to MLOps and model deployment in production systems is a plus. ","

As we build our Data Science Center of Excellence (CoE), we are looking for an entrepreneurial and technically strong Data Science Lead who can lay the foundation for a high-performing team. You will work directly with stakeholders across multiple business units to define use cases, lead model development, and ensure successful deployment and value realization. Key Responsibilities: Serve as the technical and strategic lead for the Data Science CoE. Partner with business leaders to identify use cases, assess feasibility, and define measurable outcomes. Design end-to-end data science solutions \u2014 from problem framing and data exploration to model development and deployment. Build and mentor a team of data scientists as the CoE scales. Promote best practices in experimentation, model development, versioning, documentation, and governance. Collaborate closely with engineering and data platform teams for scalable solution delivery. Evangelize data science best practices and build credibility through delivery of impactful projects. Qualifications: 10+ years of experience in applied data science, machine learning, or AI solutioning. Strong portfolio of business-impacting projects across domains such as customer analytics, personalization, marketing, pricing, operations, or product. Experience working with cloud platforms (AWS, Azure, or GCP) and modern data science tools (Python, R, SQL, etc.). Strong stakeholder management and communication skills to explain complex models in simple business terms. Prior experience in setting up data science teams, mentoring junior talent, and working in a cross-functional delivery environment. Exposure to MLOps and model deployment in production systems is a plus. ","

JOB OBJECTIVE Manager with good hands-on experience of 6+ years in developing state of the art and scalable Machine Learning models and their operationalization, leveraging off-the-shelf workbench production. KEY RESPONSIBILITIES Necessary Skills – 6+ years of experience of model development using Python/PySpark libraries. Development on Databricks or Dataiku DSS (Data Science Studio) environment would be a plus Strong experience on Spark with Scala/Python/Java Strong proficiency in building/training/evaluating state of the art machine learning models and its deployment Proficiency in Statistical and Probabilistic methods such as SVM, Decision-Trees, Bagging and Boosting Techniques, Clustering Proficiency in Core NLP techniques like Text Classification, Named Entity Recognition (NER), Topic Modeling, Sentiment Analysis, etc. Understanding of Generative AI / Large Language Models / Transformers would be a plus Hands on experience in Python data-science and math packages such as NumPy, Pandas, Sklearn, Seaborn, PyCaret, Matplotlib Proficiency in Python and common Machine Learning frameworks (TensorFlow, NLTK, Stanford NLP, PyTorch, Ling Pipe, Caffe, Keras, SparkML and OpenAI etc.) Experience of working in large teams and using collaboration tools like GIT, Jira and Confluence Good understanding of any of the cloud platform – AWS, Azure or GCP Understanding of Commercial Pharma landscape and Patient Data / Analytics would be a huge plus Should have an attitude of willingness to learn, accepting the challenging environment and confidence in delivering the results within timelines. Should be inclined towards self motivation and self-driven to find solutions for problems. Should be able to mentor and guide mid to large sized teams under him/her

Work Responsibilities The Palantir Developer will be responsible for designing and implementing modern data architecture solutions that facilitate enterprise-level transformation. Key responsibilities include: Data Architecture Design: Create and optimize modern data architectures that support advanced analytics and operational requirements. Pipelining: Develop and maintain efficient data pipelines using Palantir Foundry to ensure seamless data flow and accessibility for analytics. Advanced Analytics: Create and deploy advanced analytics products that provide actionable insights to stakeholders, enhancing decision-making processes. Artificial Intelligence Integration: Collaborate with data scientists to incorporate AI and machine learning models into data pipelines and analytics products, enabling predictive capabilities. Agentic AI Exposure: Leverage knowledge of Agentic AI to develop systems that can autonomously make decisions and take actions based on data insights, enhancing operational capabilities. Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and business analysts, to gather requirements and deliver tailored solutions. Cloud Technologies: Utilize cloud-based tools and services to enhance scalability, security, and performance of data solutions. Best Practices: Implement best practices for data governance, quality, and security to maintain data integrity and compliance with relevant regulations. Continuous Improvement: Identify opportunities for process improvements and automation to enhance operational efficiency within data ecosystems. Documentation: Maintain comprehensive documentation of data architecture designs, pipeline configurations, and analytics processes. The Team Artificial Intelligence & Data Engineering: In this age of disruption, organizations need to embrace data-driven decision-making to Deliver Enterprise Value. Our Team Leverages Data, Analytics, Robotics, And Cognitive Technologies To Uncover Insights And Drive Transformation In Business. Key Initiatives Include Data Ecosystem Implementation: Collaborate with clients to implement large-scale data ecosystems that integrate structured and unstructured data for comprehensive insights. Predictive Analytics: Utilize machine learning and predictive modeling techniques to derive actionable insights and predict future scenarios. AI Solutions Development: Work on developing AI-driven solutions that enhance data analytics capabilities, including natural language processing (NLP), computer vision, and recommendation systems. Agentic AI Development: Engage in projects that involve the development and deployment of Agentic AI systems capable of autonomous decision-making and action-taking based on real-time data. Operational Efficiency: Drive operational efficiency by utilizing automation and cognitive techniques for data management, ensuring timely and accurate reporting. Client Engagement: Engage with clients to understand their unique challenges and tailor solutions that align with their strategic objectives. Innovative Solutions: Research and implement innovative technologies and methodologies that enhance data analytics capabilities and drive business value. Training and Support: Provide training and support to clients on data tools and platforms to ensure they can maximize the value of their data assets. Qualifications Required: Education: Bachelors degree in Computer Science, Data Science, Engineering, or a related field. Experience 3+ years of hands-on experience in data extraction and manipulation using various tools and programming languages. 3+ years of experience in engineering and developing Palantir pipelines, with a strong understanding of data integration techniques. 3+ years of experience collaborating with Palantir Foundry data scientists and engineers on complex data projects. 2+ years of experience working with AI and machine learning technologies, including model development, deployment, and performance tuning. Familiarity with Agentic AI concepts and applications, including experience developing or working with autonomous systems is a plus. Technical Skills: Proficiency in programming languages such as Python, SQL, or R, along with experience in statistical analysis and machine learning techniques. Problem-Solving: Strong analytical and problem-solving skills, with the ability to think critically and creatively. Communication: Excellent interpersonal and communication skills to effectively convey technical concepts to non-technical stakeholders.

Key Responsibilities: Design and Development: Design, architect, and deploy AI/GenAI models and solutions using various technologies and frameworks (e.g., TensorFlow, PyTorch, LangChain, Vellum etc) on non-cloud infrastructure. Agentic AI: Lead the development and integration of agentic AI systems, enabling autonomous decision-making and action-taking capabilities in AI solutions. Vector Database: Design and implement vector databases (e.g., Faiss, Annoy, Hnswlib) for efficient similarity search and retrieval in AI applications. Technical Leadership: Provide technical guidance and mentorship to junior team members, ensuring high-quality deliverables and adherence to best practices. Security of LLMs: Design and implement robust security measures to prevent data poisoning, model inversion attacks, and membership inference attacks, including data encryption, access controls, model watermarking, and regular security audits. Client Engagement: Collaborate with clients to understand their AI requirements, develop tailored solutions, and deliver high-quality results. Act as a trusted technical advisor. Model Development: Develop and fine-tune AI/GenAI models for specific use cases, such as natural language processing, computer vision, or predictive analytics. Testing and Validation: Design and oversee thorough testing and validation of AI/GenAI models, including performance evaluation, bias detection, and explainability. Deployment and Maintenance: Lead the deployment of AI/GenAI models in production environments, ensuring seamless integration with existing systems and infrastructure. Knowledge Sharing: Share knowledge and expertise with the team, contributing to the development of best practices and staying up-to-date with industry trends. Lead training sessions for team members. Collaboration: Work closely with cross-functional teams, including data science, engineering, and product management, to ensure successful project delivery. Requirements: Education: Bachelor/Master's in Computer Science, AI, ML, or related fields. Experience: 8+ years of experience in engineering solutions, with a track record of delivering AI solutions. Technical Skills: Advanced Proficiency in AI/GenAI technologies, including deep learning frameworks, NLP, and computer vision. Experience with vector databases and similarity search algorithms. Experience with security measures for LLMs, including data encryption, access controls, and model watermarking. Programming Skills: Strong programming skills in languages like Python or R Communication: Excellent communication and interpersonal skills, with the ability to work effectively with clients and internal teams. Problem-Solving: Strong problem-solving skills, with the ability to analyse complex problems and develop creative solutions. Nice to have: Experience with containerization (Docker) and orchestration (Kubernetes) Nice to have: Experience with ReactJS for rapid prototyping

Requirements 5+ years of experience in machine learning, with a focus on NLP, LLMs, and computer vision Proficiency in Python and deep learning frameworks like TensorFlow, Keras, or PyTorch Experience with OCR tools or PDF processing libraries Strong understanding of AI model development and deployment Ability to work in a hybrid environment, with 2 days per week in-office Open to relocation if required Responsibilities Develop and optimize NLP and LLM models to process construction product data Implement computer vision solutions for document analysis and data extraction Collaborate with cross-functional teams to integrate AI models into production workflows Continuously improve model performance and scalability Stay updated with the latest advancements in AI and machine learning Job Details Location: Hybrid (2x a week in-office) Perks Competitive salary Unlimited PTO Hybrid work model Zomato Enterprise for lunch in the office Annual Offsite ESOPs Performance bonus up to 20% of base salary Interview Process HackerEarth Take Home Coding Test 30 MCQs (30 minutes) Technical Round 1 Bug solving (60 minutes) Manager Round Tech Deep Dive (90 minutes) Founder Round Culture round (60 minutes) Show more Show less

Model Risk Management Group (MRMG) within Global Risk Banking and Compliance Group, is responsible for the independent risk management of all American Express (AXP) models. Purpose of the Role: The successful candidate will be responsible to manage AI/ML model risk, specifically associated to the next generation Machine Learning based models for credit and fraud risk. This role will elevate model excellence, strengthen long term shareholder value, and adapt to the changing landscape of both model development innovation, external environment and heightened supervisory expectations. The specific responsibilities include : Conduct independent research and oversight of enterprise-wide models with a focus on Artificial Intelligence and Machine Learning based models for credit, fraud or other business/ risk types. Conduct gap assessments and establish robust framework to strengthen model risk controls and meet heightened regulatory standards Conduct AI/ML research to explore opportunities and set guidelines in order to address regulators, elevate model excellence and drive business impact. Communicating results to stakeholders, senior leadership and model committees Bring in external perspective around industry GSIB practices, to strengthen model risk management at American Express Critical Factors to Success: 1.Business Outcomes: Regulatory Agility: Anticipates evolving guidelines, adapts swiftly, and collaborates with regulators to shape effective risk management practices. Adaptive Resilience: To become an agile partner that anticipates risk through independent monitoring, adapts swiftly and ensures models remain robust and relevant. Holistic Integration: To break down silos and integrate MRM seamlessly across the organization by bringing along stakeholders. Continue to support and effectively engage with our first line business units as trusted advisors while also providing independent and credible challenge. Innovation: To create an innovation hub, that leads with external perspective and proactively identifies model risk in the system (publishing white papers on the same). Create the next generation of core capabilities and technological advancements that will foster innovation. Business success: Build a robust model risk management framework, appetite and strategy ensuring they accurately reflect firm s strategy, desired business outcomes and that the company remains within its risk appetite 2. Leadership Outcomes: Develop and executes a vision to strengthen model risk management for credit and fraud risk models for American Express Put enterprise thinking first, connect the role s agenda to enterprise priorities and balance the needs of customers, partners, colleagues & shareholders. Forward looking: To foster a culture that proactively anticipates future developments and draws insights from external perspectives. Bring in external perspective around industry GSIB practices, to strengthen model risk management at American Express Demonstrate learning agility, make decisions quickly and with the highest level of integrity Lead with a mindset of establishing excellence in model risk management practices Coach and develop a batch of strong model risk managers, and people leaders Experience: Preferred at least 6-7 years experience in model risk management, data science, and machine learning. Academic Background: Preferred masters degree in economics, Statistics, Machine learning, Data science Or Related Fields from Top Tier Institute. Key Functional, Technical and Leadership Skills/Capabilities: Clear understanding of AI/ML model risk management controls/practices. Strong grasp around regulatory supervisories for model risk management, and industry perspective Hands-on AI/ML model validation and preferably AI/ML model development experience. Experience in applying advanced statistical and/or quantitative techniques to solve business problems. Strong experience/ expertise with data science tools and AI/ML model development algorithms. Ability to manage key stakeholders and partners and drive agenda. Strong project management skills for driving model validation initiatives. Ability and track record to drive innovation and defining what winning looks like. Good Verbal, Written, Interpersonal skills and ability to work effectively in a team environment. Ability and track record to bring others along with you, to drive and deliver on key initiatives. Willingness and ability to effectively collaborate with Cross-Functional teams to drive validation and Project Execution. Flexibility and adaptability to work within tight deadlines and changing priorities in a dynamic environment. Ability to make quick decisions after close and effective review of data and business outcomes. Ability and experience in nurturing and developing strong talent pipeline.

We at Barracuda are at the forefront of protecting our customers from email-borne threats and data leaks. As an Analyst you will be having an opportunity to work with a core team of Threat Analysts who are specialized in stopping malicious traffic and content from reaching our customers. What you'll be working on: Analyze attack patterns and data trends, identifying drift and anomalies. Conduct root cause analysis and develop hypotheses for missed attacks. Report findings to the ML team with impact assessments. Evaluate feature importance and model effectiveness. Build reports, queries, and dashboards for insights. What you bring to the role: 0-1 year experience in Python, Scala, SQL, Databricks, and Spark. Hands-on experience in ML model development (MLLib, TensorFlow, PyTorch, Scikit). Understanding of email security (spam/phishing) is a plus. Strong analytical, communication, and adaptability skills. Willingness to work in a fast-paced, shift-based environment.

Role Overview : Hiring an ML Engineer with experience in Cloudera ML to support end-to-end model development, deployment, and monitoring on the CDP platform. Key Responsibilities : Develop and deploy models using CML workspaces Build CI/CD pipelines for ML lifecycle Integrate with governance and monitoring tools Enable secure model serving via REST APIs Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise Skills Required : Experience in Cloudera ML, Spark MLlib, or scikit-learn ML pipeline automation (MLflow, Airflow, or equivalent) Model governance, lineage, and versioning API exposure for real-time inference

Demonstrate quantitative skills and well versed with data wrangling methods. Develop, Review, Validation of Basel/Capital model on risk factors PD, LGD, Credit Portfolio) for retail and wholesale portfolios. Documentation aspect of model development is key as are the skills to present this information to peer review and independent review of model governance committees. Manage Basel Modelling, PD, LGD Model Development Develop / validate risk measurement models for credit risk management, incl. models for credit rating and scorecards. Collate, test and check independently sourced economics data (forecast and stress) and assess its robustness and fitness for purpose of model development. Demonstrate Credit Risk Model Development. Ensure adequate documentation and analysis in place for model review committee which involves peer review and independent review committees Prepare effective material for dissemination to key business stakeholders at all levels of seniority and obtain approval by the clients Support ad-hoc requests in support of the business as necessary Ensure timelines around project deliverable are met and all the stake holders are informed about the status of the projects Drive standardization of analysis and processes to gain efficiency If you thrive in a dynamic, collaborative workplace, IBM provides an environment where you will be challenged and inspired every single day. And if you relish the freedom to bring creative, thoughtful solutions to the table, there's no limit to what you can accomplish here. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise Graduate or postgraduate with a minimum of6+ years of experience in theFinance domain Proven Mathematician/Statisticianor quantitative background with knowledge of economic and econometric models (at least master's degree). Working knowledge SAS / Python and R. Exposure to Basel Modelling, PD, LGD Model Development and Validation, economic capital modelling, from any Competitoror bank / with Risk Management experience in the wholesale and retail area. Proven communication skills with technical (the team) and non-technical (senior entity management around the globe) counterparts. Proficient in high customer orientation and conflict management Working knowledge of credit risk or Basel/capital model development Ability to convert business needs into modelling needs

Job Description for Sr Data Scientist (relevant experience: 2 to 5 years) What we look for: Expertise in Big Data/ML: Should be able to build cutting edge credit/churn/usage models using advanced algorithms in a Big data/Machine Learning environment. Should have hands on experience and track record of delivering projects in individual capacity. - Process oriented: Should help in building a process that maximizes operating efficiency while maintaining risk across multiple lending cycles. There needs to be an obsession with collecting and analyzing data to drive business iterations and improvements. - Willingness to go above and beyond: For start-ups the responsibilities and needs of the business change quickly. Were looking for someone who is not afraid to take on calculated risks and can deal with ambiguity. Job Responsibilities: - Develop innovative credit risk / churn / usage models using mobile wallet transaction data, Call data, Telecom usage data, customer bureau data etc - Partner with the Data Engineering team to define the required data pipelines to build and enhance the feature bank (foundational capability) to build/deploy the various ML algorithms both for batch and real time use cases - Collaborate with credit policy/portfolio mgmt. team to drive P&L outcomes. - Building reports for model monitoring and drive enhancements Required Qualifications & Skills: Solid expertise in end-to-end risk model lifecycle management (develop, deploy, monitor) Previous hands on in credit, fraud, churn model development and deployment Previous experience in PD/EAD/LGD model development/validation Experience in CSI/PSI model monitoring process Hands on experience in data extraction using SQL/Pyspark SQL; data cleaning, feature creation and building models using Py Spark/Python on Spark; Scale will be a plus. Previous exposure to below algorithms (preferably multiple): Logistic Regression Random forest XGBOOST Markov Chain PSI/CSI for model monitoring Strategy performance tracking and swap in / swap out analysis - Strong entrepreneurial drive - Good to have: o Good business understanding of the fintech/consumer finance space o Experience in working with credit card / personal lending space, esp fintech o hands on experience in working with Telecom data

At GreenStitch , we are on a mission to revolutionise the fashion and textile industry through cutting-edge climate-tech solutions. We are looking for a highly skilled Data Scientist-I with expertise in NLP and Deep Learning to drive innovation in our data-driven applications. This role requires a strong foundation in AI/ML, deep learning, and software engineering to develop impactful solutions aligned with our sustainability and business objectives. The ideal candidate demonstrates up-to-date expertise in Deep Learning and NLP and applies it to the development, execution, and improvement of applications. This role involves supporting and aligning efforts to meet both customer and business needs. You will play a key role in building strong relationships with stakeholders, identifying business challenges, and implementing AI-driven solutions. You should be adaptable to competing demands, organisational changes, and new responsibilities while upholding GreenStitchs mission, values, and ethical standards. What Youll Do: AI-Powered Applications: Build and deploy AI-driven applications leveraging Generative AI and NLP to enhance user experience and operational efficiency. Model Development: Design and implement deep learning models and machine learning pipelines to solve complex business challenges. Cross-Functional Collaboration: Work closely with product managers, engineers, and business teams to identify AI/ML opportunities. Innovation & Experimentation: Stay up-to-date with the latest AI/ML advancements , rapidly prototype solutions, and iterate on ideas for continuous improvement . Scalability & Optimisation: Optimise AI models for performance, scalability, and real-world impact , ensuring production readiness. Knowledge Sharing & Thought Leadership: Contribute to publications, patents, and technical forums; represent GreenStitch in industry and academic discussions . Compliance & Ethics: Model compliance with company policies and ensure AI solutions align with ethical standards and sustainability goals. Communication: Translate complex AI/ML concepts into clear, actionable insights for business stakeholders. What Youll Bring: Education: Bachelors/Masters degree in Data Science, Computer Science, AI, or a related field . Experience: 1-3 years of hands-on experience in AI/ML, NLP, and Deep Learning . Technical Expertise: Strong knowledge of transformer-based models (GPT, BERT, etc.), deep learning frameworks ( TensorFlow, PyTorch ), and cloud platforms ( Azure, AWS, GCP ). Software Development: Experience in Python, Java , or similar languages, and familiarity with MLOps tools . Problem-Solving: Ability to apply AI/ML techniques to real-world challenges with a data-driven approach. Growth Mindset: Passion for continuous learning and innovation in AI and climate-tech applications. Collaboration & Communication: Strong ability to work with cross-functional teams , communicate complex ideas, and drive AI adoption. Why GreenStitch GreenStitch is at the forefront of climate-tech innovation, helping businesses in the fashion and textile industry reduce their environmental footprint. By joining us, you will: Work on high-impact AI projects that contribute to sustainability and decarbonisation. Be part of a dynamic and collaborative team committed to making a difference. Enjoy a flexible, hybrid work model that supports professional growth and work-life balance. Receive competitive compensation and benefits , including healthcare, parental leave, and learning opportunities .

Skillset Requirements: Technical Lead 510 years of relevant experience in data science Minimum 5 years of experience in Python programming At least 3 years of experience in machine learning and NLP Minimum 2 years of experience with LLMs and GenAI At least 2 years of experience with any database 1+ years of experience deploying ML models/Python applications on Azure or AWS Familiarity with LLMOps is a plus Role and Responsibilities: Lead development, deployment, and optimization of ML, NLP models, and LLM/GenAI solutions Develop and maintain Python-based ML pipelines and manage associated databases Deploy AI models on Azure/AWS, ensuring performance and scalability Mentor team members and stay updated on advancements in ML, NLP, and GenAI Implement LLMOps best practices for efficient model lifecycle management

Model Development & Experimentation: Assist in the research, design, development, and implementation of machine learning and deep learning models. Contribute to the selection and fine-tuning of algorithms (e.g., supervised, unsupervised, reinforcement learning, neural networks, NLP models, computer vision models). Conduct experiments to evaluate model performance, identify limitations, and propose improvements. Data Preparation & Analysis: Collect, clean, preprocess, and transform large and complex datasets to ensure high-quality training data for AI models. Perform exploratory data analysis to uncover insights, patterns, and trends that inform model development. Utilize data visualization techniques to present findings clearly. Research & Learning: Conduct literature reviews on current AI trends, state-of-the-art models, and best practices. Stay updated with the latest advancements in AI, machine learning, and deep learning. Contribute to brainstorming sessions and provide innovative ideas for AI solutions. Collaboration & Documentation: Collaborate effectively with AI engineers, data scientists, researchers, and cross-functional teams to understand project requirements and objectives. Assist in maintaining detailed records of AI experiments, model development processes, code, and results. Support in preparing technical reports and presentations to communicate findings to internal stakeholders. Code Implementation & Optimization: Write clean, efficient, and well-documented code in Python (or other relevant languages) for AI model development and deployment. Assist in debugging and optimizing AI model performance and efficiency.

We re looking for a Jr AI Security Architect to join our growing Security Architecture team. This role will support the design, implementation, and protection of AI/ML systems, models, and datasets. The ideal candidate is passionate about the intersection of artificial intelligence and cybersecurity, and eager to contribute to building secure-by-design AI systems that protect users, data, and business integrity. Key Responsibilities Secure AI Model Development - Partner with AI/ML teams to embed security into the model development lifecycle, including during data collection, model training, evaluation, and deployment. - Contribute to threat modeling exercises for AI/ML pipelines to identify risks such as model poisoning, data leakage, or adversarial input attacks. - Support the evaluation and implementation of model explainability, fairness, and accountability techniques to address security and compliance concerns. - Develop and train internal models for security purposes Model Training & Dataset Security - Help design controls to ensure the integrity and confidentiality of training datasets, including the use of differential privacy, data validation pipelines, and access controls. - Assist in implementing secure storage and version control practices for datasets and model artifacts. - Evaluate training environments for exposure to risks such as unauthorized data access, insecure third-party libraries, or compromised containers. AI Infrastructure Hardening - Work with infrastructure and MLOps teams to secure AI platforms (e.g., MLFlow, Kubeflow, SageMaker, Vertex AI) including compute resources, APIs, CI/CD pipelines, and model registries. - Contribute to security reviews of AI-related deployments in cloud and on-prem environments. - Assist in automating security checks in AI pipelines, such as scanning for secrets, validating container images, and enforcing secure permissions. Secure AI Integration in Products - Participate in the review and assessment of AI/ML models embedded into customer-facing products to ensure they comply with internal security and responsible AI guidelines. - Help develop misuse detection and monitoring strategies to identify model abuse (e.g., prompt injection, data extraction, hallucination exploitation). - Support product security teams in designing guardrails and sandboxing techniques for generative AI features (e.g., chatbots, image generators, copilots). Knowledge Sharing & Enablement - Assist in creating internal training and security guidance for data scientists, engineers, and developers on secure AI practices. - Help maintain documentation, runbooks, and security checklists specific to AI/ML workloads. - Stay current on emerging AI security threats, industry trends, and tools; contribute to internal knowledge sharing. Qualifications - 3-4 years of experience in LLM and 7-10 years of experience in cybersecurity, machine learning, or related fields. - Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and MLOps tools (e.g., MLFlow, Airflow, Kubernetes). - Familiarity with AI models and Supplychain risks - Understanding of common AI/ML security threats and mitigations (e.g., model inversion, adversarial examples, data poisoning). - Experience working with cloud environments (AWS, GCP, Azure) and securing workloads. - Some knowledge of responsible AI principles, privacy-preserving ML, or AI compliance frameworks is a plus. Soft Skills - Strong communication skills to collaborate across engineering, data science, and product teams. - A continuous learning mindset and willingness to grow in both AI and security domains. - Problem-solving approach with a focus on practical, scalable solutions.

About us: As a Fortune 50 company with more than 400,000 team members worldwide, Target is an iconic brand and one of America's leading retailers. Joining Target means promoting a culture of mutual care and respect and striving to make the most meaningful and positive impact. Becoming a Target team member means joining a community that values different voices and lifts each other up . Here, we believe your unique perspective is important, and you'll build relationships by being authentic and respectful. Overview about TII At Target, we have a timeless purpose and a proven strategy. And that hasn t happened by accident. Some of the best minds from different backgrounds come together at Target to redefine retail in an inclusive learning environment that values people and delivers world-class outcomes. That winning formula is especially apparent in Bengaluru, where Target in India operates as a fully integrated part of Target s global team and has more than 4,000 team members supporting the company s global strategy and operations. Pyramid overview A role with Target Data Science & Engineering means the chance to help develop and manage state of the art predictive algorithms that use data at scale to automate and optimize decisions at scale. Whether you join our Statistics, Optimization or Machine Learning teams, you ll be challenged to harness Target s impressive data breadth to build the algorithms that power solutions our partners in in Marketing, Supply Chain Optimization, Network Security and Personalization rely on Every Scientist on Target s Data Sciences team can expect modeling and data science, software/product development of highly performant code for Model Performance, and to elevate Target s culture and apply retail domain knowledge. About the role As a Lead Data Scientist, you ll influence by interacting with the Data Sciences team, Product teams, Scientist/Engineer individual contributors from other pillars, and business partners. You will perform within the scale and scope of your role by defining solutions and beginning to identify problems to solve and contribute to Data Sciences and Target s culture by modeling and contributing to the culture. You ll get the opportunity to use your expertise in one or more of the following areasmachine learning, probability theory & statistics, optimization theory, Simulation, Econometrics, Deep Learning, Natural Language processing or computer vision. We will look to you to own design and implementation of an algorithmic solution (e.g., recommendation or forecasting algorithm), including data understanding, feature engineering, model development, validation and testing, and deployment to a production environment. You ll drive development of problem statements that capture the business considerations, define metrics/measurement to validate model performance, and drive feasibility study with data requirements and potential solutions approaches to be considered. You ll evaluate tradeoffs of simple vs complex models/solutions in determining the right technique to employ for a business problem and develop and maintain a nuanced understanding of the data generated by the business, including fundamental limitations of the data. You ll leverage your proficiency in one or more approved programming languages (Java, Scala, Python, R), and ensure foundational programming principles in developing code (best practices, writes unit tests, code organization, basics of CI/CD etc.) are followed in developing the team s products/models. You ll not only stitch together basic data pipelines for a given problem and own design and implementation of individual components within Data Science/Tech applications, but also articulate the technical strategy, value of technology, and impact on the business. As you do so, you ll collaborate with engineers, scientists, and business partners/product owners to create algorithmic solutions that are performant and integrated into applications. We ll look to you to mentor and provide technical support within a team, including mentoring junior team members, and present your work and your team s work to business partners and other Data Sciences teams. With a deeper understanding of your functional area of responsibility, you ll support agile ceremonies, collaborate with peers across multiple products, communicate and collaborate with business partners, and demonstrate an understanding of areas outside your scope of responsibility. The exciting part of retailIt s always changing! Core responsibilities of this job are described within this job description. Job duties may change at any time due to business needs. About you: 4-year degree in Quantitative disciplines (Science, Technology, Engineering, Mathematics) and 6+ years of professional experience or equivalent industry experience Master s degree in Quantitative disciplines (Science, Technology, Engineering, Mathematics) Good knowledge and experience developing optimization, simulation, and statistical models Strong analytical thinking skills. Ability to creatively solve business problems, innovating new approaches where required Strong hands-on programming skills in Python, SQL, Hadoop/Hive. Additional knowledge of Spark, Scala, R, Java desired but not mandatory Good working knowledge of mathematical and statistical concepts, MILP, algorithms, and computational complexity Passion for solving interesting and relevant real-world problems using a data science approach Experience in implementing advanced statistical techniques like regression, clustering, PCA, forecasting (time series) etc. Able to produce reasonable documents/narrative suggesting actionable insights Excellent communication skills. Ability to clearly tell data driven stories through appropriate visualizations, graphs, and narratives Self-driven and results oriented; able to meet tight timelines Strong team player with ability to collaborate effectively across geographies/time zones Know More About us here: Life at Target- https://india.target.com/ Benefits- https://india.target.com/life-at-target/workplace/benefits Culture- https://india.target.com/life-at-target/belonging

About us: As a Fortune 50 company with more than 400,000 team members worldwide, Target is an iconic brand and one of America's leading retailers. Joining Target means promoting a culture of mutual care and respect and striving to make the most meaningful and positive impact. Becoming a Target team member means joining a community that values different voices and lifts each other up. Here, we believe your unique perspective is important, and you'll build relationships by being authentic and respectful. Overview about TII At Target, we have a timeless purpose and a proven strategy. And that hasn t happened by accident. Some of the best minds from different backgrounds come together at Target to redefine retail in an inclusive learning environment that values people and delivers world-class outcomes. That winning formula is especially apparent in Bengaluru, where Target in India operates as a fully integrated part of Target s global team and has more than 4,000 team members supporting the company s global strategy and operations. Pyramid overview A role with Target Data Science & Engineering means the chance to help develop and manage state of the art predictive algorithms that use data at scale to automate and optimize decisions at scale. Whether you join our Statistics, Optimization or Machine Learning teams, you ll be challenged to harness Target s impressive data breadth to build the algorithms that power solutions our partners in in Marketing, Supply Chain Optimization, Network Security and Personalization rely on Every Scientist on Target s Data Sciences team can expect modeling and data science, software/product development of highly performant code for Model Performance, and to elevate Target s culture and apply retail domain knowledge. Position Overview: As a Senior Data Scientist, you will be involved in end-to-end development of Ad-Tech products and capabilities that fulfil strategic priorities and power growth of Roundel, Target s Retail media business. You will leverage understanding of data and algorithms to build prototypes and run experiments to evaluate them against given specifications. As you follow agile processes, you will implement and deploy a scalable data science solution by using MLOps best practices across model development life cycle. You will collaborate with product and business partners to seek feedback on effectiveness of solution and identify future opportunities for enhancements. You will work with your peers to create a well-maintainable & tested codebase with relevant documentation. The exciting part of retail and mediaIt s always changing! Core responsibilities of this job are described within this job description. Job duties may change at any time due to business needs. About You: 4-year degree in Quantitative disciplines (Science, Technology, Engineering, Mathematics) or equivalent experience 3+ years of professional experience or equivalent industry experience Good knowledge and experience developing optimization, simulation and statistical models Strong analytical thinking skills. Ability to creatively solve business problems, innovating new approaches, where required Strong hands-on programming skills in Python, SQL, Spark, Hadoop/Hive . Good working knowledge of mathematical and statistical concepts, MILP, algorithms and computational complexity Passion for solving interesting and relevant real-world problems using a data science approach Experience in implementing advanced statistical techniques like r egression, clustering, PCA, forecasting (time series) etc. Able to produce reasonable documents/narrative suggesting actionable insights Excellent communication skills. Ability to clearly tell data driven stories through appropriate visualizations, graphs and narratives Self-driven and results oriented; able to meet tight timelines Strong team player with ability to collaborate effectively across geographies/time zones Know More About Us here: Life at Target - https://india.target.com/ Benefits - https://india.target.com/life-at-target/workplace/benefits Culture- https://india.target.com/life-at-target/belonging

Lead the fine-tuning and domain adaptation of open-source LLMs (eg, LLaMA 3) using frameworks like vLLM, HuggingFace, DeepSpeed, and PEFT techniques. Develop data pipelines to ingest, clean, and structure cybersecurity data, including threat intelligence reports, CVEs, exploits, malware analysis, and configuration files. Collaborate with cybersecurity analysts to build taxonomy and structured knowledge representations to embed into LLMs. Drive the design and execution of evaluation frameworks specific to cybersecurity tasks (eg, classification, summarization, anomaly detection). Own the lifecycle of model development including training, inference optimization, testing, and deployment. Provide technical leadership and mentorship to a team of ML engineers and researchers. Stay current with advances in LLM architectures, cybersecurity datasets, and AI-based threat detection. Advocate for ethical AI use and model robustness, especially given the sensitive nature of cybersecurity data Required Skills: 5+ years of experience in machine learning , with at least 2 years focused on LLM training or fine-tuning . Strong experience with vLLM , HuggingFace Transformers, LoRA / QLoRA , and distributed training techniques . Proven experience working with cybersecurity data \u2014ideally including MITRE ATT&CK, CVE/NVD databases, YARA rules, Snort/Suricata rules, STIX/TAXII, or malware datasets . Proficiency in Python , ML libraries ( PyTorch , Transformers), and MLOps practices. Familiarity with prompt engineering, RAG (Retrieval-Augmented Generation), and vector stores like FAISS or Weaviate . Demonstrated ability to lead projects and collaborate across interdisciplinary teams. Excellent problem-solving skills and strong written & verbal communication. Nice to Have Experience deploying models via vLLM in production environments with FastAPI or similar APIs. Knowledge of cloud-based ML training (AWS/GCP/Azure) and GPU infrastructure. Background in reverse engineering, malware analysis, red teaming, or threat hunting. Publications, open-source contributions, or technical blogs in the intersection of AI and cybersecurity.