31 Ml Ops Jobs

The Lead MLOps Engineer will be responsible for leading technology initiatives aimed at improving business value andoutcomes in the areas of digital marketing and commercial analytics through the adoption of Artificial Intelligence (AI) enabled solutions. Working with cross-functional teams across AI projects to operationalize data science models to deployed scalable solutions delivering business value. They should be inquisitive and bring an innovate mindset to work every day, researching, proposing, and implementing MLOps process improvements, solution ideas and ways of working to be more agile, lean and productive. Provide leadership and technical expertise in operationalizing machine learning models, bridging the gap between data science and IT operations. Key responsibilities include designing, implementing, and optimizing MLOps infrastructure, building CI/CD pipelines for ML models, and ensuring the security and scalability of ML systems. Key Responsibilities Architect & Deploy: Design and manage scalable ML infrastructure on Azure (AKS), leveraging Infrastructure as Code principles. Automate & Accelerate: Build and optimize CI/CD pipelines with GitHub Actions for seamless software, data, andmodel delivery. Engineer Performance: Develop efficient and reliable data pipelines using Python and distributed computing frameworks. Ensure Reliability: Implement solutions for deploying and maintaining ML models in production. Collaborate & Innovate: Partner with data scientists and engineers to continuously enhance existing MLOps capabilities. Key Competencies: Experience: A minimum of 5+ years of experience in software engineering, data science, or a related field with experience in MLOps is typically required. Education: A bachelor's or master's degree in Computer Science / Engineering. Soft Skills: Strong analytical and problem-solving skills, excellent communication and collaboration skills, and the ability to work in a fast-paced environment are highly valued. Azure & AKS: Deep hands-on experience. IaC & CI/CD: Mastery of Terraform/Bicep & GitHub Actions. Data Engineering: Advanced Python & Spark for complex pipelines. ML Operations: Proven ability in model serving & monitoring. Problem Solver: Adept at navigating complex technical challenges and delivering solutions.

Must have MLops Python MySQL Model Lifecycle PYspark GCP(Bigquery , Dataproc & Airflow) Experience 3–14 Years Location Chennai, Hyderabad 2–3 Days Onsite Shift 9:00 to 6:00 PM NP- Immediate Joiners Preferred, max 30 days Required Candidate profile Must have MLops Python MySQL Model Lifecycle PYspark GCP(Bigquery , Dataproc & Airflow) Experience 3–14 Years Location Chennai, Hyderabad 2–3 Days Onsite

We are looking for a Junior Machine Learning Engineer with 4 to 6 years of experience to join our AI/ML team. The candidate will wok on reinforcement learning agents, end-to-end ML workflows, and MLOps practices to build, deploy, and monitor scalable AI solutions. Key Responsibilities: Develop and deploy ML models and RL agents (e.g., Q-learning, policy gradients). Preprocess data, perform feature engineering, and tune models. Operationalize models using Docker, CI/CD (GitHub Actions, Jenkins). Monitor model performance and system health in production. Collaborate with data scientists, engineers, and DevOps teams. Stay updated on trends in RL, AI, and MLOps. Required Skills: Bachelors/Masters in Computer Science or related field. Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn). Hands-on experience with RL frameworks (OpenAI Gym, Stable Baselines). Familiarity with Docker, Git, CI/CD tools, and REST APIs. Understanding of ML lifecycle and cloud deployment (AWS/GCP/Azure). Preferred Skills: Experience with SageMaker, Vertex AI, or similar tools. Knowledge of deep learning, NLP, or microservices. Strong analytical and collaborative skills.

Purpose Senior DevOps engineer is responsible for automating and improving the collaboration between software development and IT operations teams, ensuring faster and more reliable software releases. Should be a quick learner with exposure to AIOps. DevOps, MLOps and some knowledge in CloudOps. and should be an expert in handling python & .Net based projects, docker, Kubernetes, podman. Job Tasks: CI/CD Pipeline Management Design, construct, install, test and maintain Infrastructure as Code Containerization Deployment and Release Management » Security Integrations » Automation and Scripting » Monitoring and Analytics » disaster recovery procedures Key Responsibilities Design, implement, and manage scalable and reliable infrastructure for GenAI-based applications. Collaborate with development teams to ensure seamless integration and deployment of GenAI models and services. Automate and optimize CI/CD pipelines to support rapid development and deployment cycles (multi cloud). Monitor and troubleshoot system performance, ensuring high availability and reliability of GenAI applications. Implement security best practices and ensure compliance with industry standards. Mentor and guide junior DevOps engineers, fostering a culture of continuous improvement and innovation. Stay updated with the latest trends and advancements in GenAI and DevOps technologies. Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Proven experience as a DevOps Engineer, with a focus on GenAI-based implementations. Strong knowledge of cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes). Proficiency in scripting and automation tools (Python, Bash, Ansible, Terraform). Experience with CI/CD tools (Jenkins, GitLab CI, CircleCI) and version control systems (Git). Solid understanding of networking, security, and system administration. Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment. Strong communication and leadership skills.

Roles and Responsibilities Develop and maintain Microservice architecture and API management solutions using REST and gRPC for seamless deployment of AI solutions. Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization. Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models. Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements. Familiarity with tools like Terraform, CloudFormation, and Pulumi for efficient infrastructure management. Create and manage CI/CD pipelines using Git-based platforms (e.g., GitHub Actions, Jenkins) to ensure streamlined development workflows. Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments. Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment. Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development. Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC. Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs. Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling. Design and execute rigorous A/B tests for machine learning models, analyzing results to drive strategic improvements and decisions. Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function. Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment. Technical Skills: Advanced proficiency in Python . Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques Experience with big data processing using Spark for large-scale data analytics Version control and experiment tracking using Git and MLflow Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing. DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations. LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management. MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining. Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems. LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security. General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP. Experience in creating LLD for the provided architecture. Experience working in microservices based architecture. Domain Expertise: Deep understanding of ML and LLM development lifecycle, including fine-tuning and evaluation Expertise in feature engineering, embedding optimization, and dimensionality reduction Advanced knowledge of A/B testing, experimental design, and statistical hypothesis testing Experience with RAG systems, vector databases, and semantic search implementation Proficiency in LLM optimization techniques including quantization and knowledge distillation Understanding of MLOps practices for model deployment and monitoring Professional Competencies: Strong analytical thinking with ability to solve complex ML challenges Excellent communication skills for presenting technical findings to diverse audiences Experience translating business requirements into data science solutions Project management skills for coordinating ML experiments and deployments Strong collaboration abilities for working with cross-functional teams Dedication to staying current with latest ML research and best practices Ability to mentor and share knowledge with team members

WhatYou llDo Build, Refine and Use ML Engineering platforms and components. Scaling machine learning algorithms to work on massive data sets and strict SLAs. Build and orchestrate model pipelines including feature engineering, inferencing and continuous model training. Implement ML Ops including model KPI measurements, tracking, model drift & model feedback loop. Collaborate with client facing teams to understand business context at a high level and contribute in technical requirement gathering. Implement basic features aligning with technical requirements. Write production-ready code that is easily testable, understood by other developers and accounts for edge cases and errors. Ensure highest quality of deliverables by following architecture/design guidelines, coding best practices, periodic design/code reviews. Write unit tests as well as higher level tests to handle expected edge cases and errors gracefully, as well as happy paths. Uses bug tracking, code review, version control and other tools to organize and deliver work. Participate in scrum calls and agile ceremonies, and effectively communicate work progress, issues and dependencies. Consistently contribute in researching & evaluating latest architecture patterns/technologies through rapid learning, conducting proof-of-concepts and creating prototype solutions. WhatYou llBring A master s or bachelor s degree in Computer Science or related field from a top university. 5+ years hands-on experience in ML development. Good fundamentals of machine learning Strong programming expertise in Python, PySpark/Scala. Expertise in crafting ML Models for high performance and scalability. Experience in implementing feature engineering, inferencing pipelines, and real time model predictions. Experience in ML Ops to measure and track model performance, experience working with MLFlow Experience with Spark or other distributed computing frameworks. Experience in ML platforms like Sage maker, Kubeflow. Experience with pipeline orchestration tools such Airflow. Experience in deploying models to cloud services like AWS, Azure, GCP, Azure ML. Expertise in SQL, SQL DBs. Knowledgeable of core CS concepts such as common data structures and algorithms. Collaborate well with teams with different backgrounds / expertise / functions. Additional Skills Understanding of DevOps, CI / CD, data security, experience in designing on cloud platform; Experience in data engineering in Big Data systems

Position Summary This Requisition is for Employee Referral Campaign and JD is Generic. Position Summary: Build scalable, effective programs to improve communication between Life Sciences companies and their customers. Job Responsibilities We are seeking a high-energy and innovative leader to join our Omnichannel Centre of Excellence to develop new, specialized capabilities for Axtria, and to accelerate the companys growth by supporting our clients omnichannel marketing strategies. Axtria CustomerIQTM is the most advanced omnichannel-driven customer engagement solution for the life sciences industry. It enables personalized interactions, at scale to optimize commercial activity. Axtria CustomerIQTM combines the power of AI/ML-based analytics with a unique knowledge of the customer journey to recommend Next Best Experiences (NBX) through Next Best Action (NBA) recommendations, significantly improving the interaction between the life sciences company and its customers. Axtria CustomerIQTM provides a complete, continuous 360 view of the customer while automating and optimizing the efficiency of both sales and marketing channels, driving higher revenue. Education Fellow Programme - Engineering Work Experience Following are the skills we hire for Our Omni Channel Team. You experience could be a fit for one of the relevant skills. Specific JD for relevant skills will be discussed by Recruiters. Data Engineer: 5- 8 years experience in data engineering, consulting, and/or technology implementation roles. Responsible for omnichannel data engineering engagements that leverage Axtrias custom data management platform or clients own technology stacks. Drive integration of traditional & digital marketing data to create a 360-degree view of the customer. Data Scientist: 5-8 years of experience in the entire model development pipeline from ideation to deployment, testing & monitoring, and different ML architectures (e.g., supervised, unsupervised, reinforcement learning, etc. Responsible for the end-to-end deployment of predictive models including scoping, testing, implementation, maintenance, tracking, and optimization of predictive models and executing statistical and data mining techniques (e.g., hypothesis testing, machine learning, and retrieval processes) on large data sets to identify trends, figures, and other relevant information. Business Analyst: 5-8 years of experience in marketing analytics. The primary role is to gather business requirements related to omnichannel marketing processes, built-in Axtrias proprietary CustomerIQ technology platform, or in clients selected decision engine technologies. Success in the role will lead to additional responsibilities to engage and advise clients, guide CustomerIQ product feature designs, develop new processes and standards, innovate new algorithm approaches, and more. Behavioural Competencies Customer focus Problem solving Learning on the fly Drive for result Technical Competencies AIML ML Data Science Python Lifescience Knowledge ML Ops MMx Marketing Mix Pharma Data Analytics

Role & responsibilities Key Responsibilities Architect and implement AI/ML/GenAI pipelines , automating end-to-end workflows from data ingestion to model deployment and monitoring. Develop scalable, production-grade APIs and services using FastAPI, Flask , or similar frameworks for AI/LLM model inference. Design and maintain containerized AI applications using Docker and Kubernetes . Operationalize Large Language Models (LLMs) and other GenAI models via cloud-native deployment (e.g., Azure ML, AWS Sagemaker, GCP Vertex AI). Manage and monitor model performance post-deployment, applying concepts of MLOps and LLMOps including model versioning, A/B testing, and drift detection. Build and maintain CI/CD pipelines for rapid and secure deployment of AI solutions using tools such as GitHub Actions, Azure DevOps, GitLab CI . Implement security, governance, and compliance standards in AI pipelines. Optimize model serving infrastructure for speed, scalability, and cost-efficiency. Collaborate with AI researchers to translate prototypes into robust production-ready solutions. Shift: 11am-8pm

ZS s Beyond Healthcare Analytics (BHCA) Team is shaping one of the key growth vector area for ZS, Beyond Healthcare engagement, comprising of clients from industries like Quick service restaurants, Technology, Food & Beverage, Hospitality, Travel, Insurance, Consumer Products Goods & other such industries across North America, Europe & South East Asia region. BHCA India team currently has presence across New Delhi, Pune and Bengaluru offices and is continuously expanding further at a great pace. BHCA India team works with colleagues across clients and geographies to create and deliver real world pragmatic solutions leveraging AI SaaS products & platforms, Generative AI applications, and other Advanced analytics solutions at scale. What You ll Do: Build, Refine and Use ML Engineering platforms and components. Scaling machine learning algorithms to work on massive data sets and strict SLAs. Build and orchestrate model pipelines including feature engineering, inferencing and continuous model training. Implement ML Ops including model KPI measurements, tracking, model drift & model feedback loop. Collaborate with client facing teams to understand business context at a high level and contribute in technical requirement gathering. Implement basic features aligning with technical requirements. Write production-ready code that is easily testable, understood by other developers and accounts for edge cases and errors. Ensure highest quality of deliverables by following architecture/design guidelines, coding best practices, periodic design/code reviews. Write unit tests as well as higher level tests to handle expected edge cases and errors gracefully, as well as happy paths. Uses bug tracking, code review, version control and other tools to organize and deliver work. Participate in scrum calls and agile ceremonies, and effectively communicate work progress, issues and dependencies. Consistently contribute in researching & evaluating latest architecture patterns/technologies through rapid learning, conducting proof-of-concepts and creating prototype solutions. What You ll Bring A master's or bachelor s degree in Computer Science or related field from a top university. 4+ years hands-on experience in ML development. Good understanding of the fundamentals of machine learning Strong programming expertise in Python, PySpark/Scala. Expertise in crafting ML Models for high performance and scalability. Experience in implementing feature engineering, inferencing pipelines, and real time model predictions. Experience in ML Ops to measure and track model performance, experience working with MLFlow Experience with Spark or other distributed computing frameworks. Experience in ML platforms like Sage maker, Kubeflow. Experience with pipeline orchestration tools such Airflow. Experience in deploying models to cloud services like AWS, Azure, GCP, Azure ML. Expertise in SQL, SQL DB's. Knowledgeable of core CS concepts such as common data structures and algorithms. Collaborate well with teams with different backgrounds / expertise / functions

Architect of Solutions: Lead the design, development, and enhancement of scalable ML Ops/Data pipelines and Data Products on cloud-native platforms. Technical Expertise: Demonstrate your expertise in Python, Pyspark, Docker, Kubernetes, and AWS ecosystems to deliver exceptional solutions. Collaborative Spirit: Work hand-in-hand with global and diverse Agile teams, from data to design, to overcome technical data challenges. Innovate & Inspire: Stay ahead of the curve by integrating the latest industry trends and innovations into your work such as GenAI. Essential Skills/Experience A proactive mindset and enthusiasm for Agile environments. Strong hands-on experience with cloud providers and services. Experience in performance tuning SQL and ML Ops data pipelines. Extensive experience in troubleshooting data issues, analyzing end-to-end data pipelines, and working with users in resolving issues. Masterful debugging and testing skills to ensure excellence in execution. Inspiring communication abilities that elevate team collaboration. Experience of structured, semi-structured (XML, JSON), and unstructured data handling including extraction and ingestion via web-scraping and FTP/SFTP. Production experience delivering CI/CD pipelines (Github, Jenkins, DataOps.Live). Cloud DevOps Engineer who can develop, test, and maintain CICD Pipeline using Terraform, cloud formation. Remain up to date with the latest technologies, like GenAI / AI platforms and FAIR scoring to improve outcomes.

Role & responsibilities Flexibility to work in US shift is essential. Design and implement scalable automation frameworks and tools to streamline business and engineering processes. Lead the development and deployment of machine learning models and AI services tailored for our SaaS platform. Work closely with cross-functional teams to identify opportunities for automation and streamline workflows to improve process efficiency. Architect, write, and maintain clean, efficient Python code for AI pipelines and microservices. Work with large datasets: data preprocessing, model training, evaluation, and optimization. Drive the implementation of AI/ML features such as predictive analytics, intelligent recommendations, and automated decision-making. Ensure robust monitoring, testing, and CI/CD practices for AI models in production. Guide and mentor junior team members on AI best practices and coding standards. Stay current with the latest AI research and industry trends and evaluate their relevance for product innovation. Continuously evaluate and optimize existing processes to improve performance, reduce costs, and enhance scalability. Preferred candidate profile Bachelors or master’s degree in computer science, Data Science, Artificial Intelligence, or a related field. 5+ years of professional experience in Python development. 3+ years of hands-on experience in machine learning, deep learning, or applied AI. Preferred experience supporting in the real estate and property management industry. Proven track record of deploying ML models in production environments, preferably for SaaS applications. Familiarity with ML Ops, model lifecycle management, and data versioning tools (e.g., MLflow, DVC). Excellent problem-solving skills and the ability to work independently in a dynamic team environment. Experience with generative AI (e.g., GPT, LLM fine-tuning or prompt engineering). Background in NLP, computer vision, or recommender systems. Experience working in Agile environments with tools like Jira, Confluence, or Notion.

- Data Scientist -6+ years of experience in data science, with at least 2 years focused on LLMs or Generative AI. -Proven implementation experience in Data Science, Machine Learning, Deep Learning, and NLP for multiple domains. -Strong programming skills in Python, with experience in libraries such as Transformers (Hugging Face), PyTorch, or TensorFlow. -Hands-on experience with fine-tuning, prompt engineering, RAG (Retrieval-Augmented Generation), and LLM evaluation. -Familiarity with vector databases and embedding techniques. -Experience deploying models using APIs, Docker, and cloud platforms -Strong analytical, problem-solving, and communication skills. -Experience in ML Ops, Model deployment, Model lifecycle and management

We are seeking an experienced AI/ML Architect to lead the design, development, and deployment of Generative AI solutions. This role requires a deep understanding of AI/ML architectures, technical leadership, and the ability to design robust, scalable, and production-ready systems. The ideal candidate will have extensive experience in cloud platforms like GCP and optionally AWS, Azure, or equivalent tools, combined with hands-on expertise in MLOps, containerization, data processing, and advanced model optimization . You will work closely with cross-functional teams, technical leadership, and stakeholders to implement state-of-the-art AI solutions that solve real-world challenges and drive business value. Roles & Responsibilities 1) Technical Leadership Lead the technical design and architecture of complex Generative AI systems. Ensure solutions align with business objectives, scalability requirements, and technical feasibility. Guide development teams through best practices , architecture reviews, and technical decision-making processes. 2) Solution Architecture Design and develop end-to-end Generative AI solutions , including data pipelines , model training, deployment, and real-time monitoring. Utilize MLOps tools and frameworks to automate workflows, ensuring scalable and repeatable deployments. Architect robust solutions using GCP and optionally AWS, Azure, or open-source frameworks. Design, train, and fine-tune AI/ML models , especially Generative AI and Large Language Models (LLMs) , to address specific use cases. Build conversational AI solutions and chatbots using frameworks such as LangChain , RAG (Retrieval-Augmented Generation) , and Chain-of-Thought (COT) prompting . 3) Production Deployment Lead the deployment of Generative AI models into production environments . Optimize deployment pipelines leveraging tools like Docker, Kubernetes , and cloud-native services for orchestration. Ensure seamless integration of GenAI solutions into existing CI/CD pipelines. 4) Data Processing & Feature Engineering Build scalable ETL workflows for managing structured, semi-structured, and unstructured data. Implement data wrangling , preprocessing, and feature engineering pipelines to prepare data for Generative AI applications. Optimize workflows to extract meaningful insights from large datasets . 5) Model Optimization Identify and implement optimization strategies such as hyperparameter tuning , feature engineering , and model selection for performance enhancement. Focus on computational efficiency and scaling models to production-level performance. 6) Pilot/POCs Development Drive the design and development of Proof of Concepts (POCs) and pilot projects to address customer requirements. Collaborate with delivery and product teams to scale successful pilots to production-grade solutions. 7) Evangelization Promote and drive the adoption of Generative AI solutions across customer and delivery teams. Provide technical leadership and mentorship to teams working on GenAI projects. Conduct workshops, training sessions, and knowledge-sharing initiatives to enable stakeholders. 8) Continuous Improvement Stay at the forefront of AI advancements , frameworks, and tools, including emerging concepts in Generative AI. Explore and evaluate techniques like Reinforcement Learning from Human Feedback (RLHF) and REACT (Retrieve, Extract, Adapt, Construct, Think) frameworks to enhance GenAI applications. Required Skills & Qualifications 10+ years of experience in AI/ML architecture, model development, and production deployment Proven expertise in designing, implementing, and scaling Generative AI and LLM-based solutions Hands-on experience with frameworks like LangChain , Retrieval-Augmented Generation (RAG) , and advanced prompting techniques Proficiency in advanced techniques such as embeddings and Chain-of-Thought (COT) prompting Experience working with cloud platforms , primarily GCP , with optional experience in AWS or Azure Strong understanding of MLOps tools, pipelines, and model monitoring in production Proficiency in Python and SQL for model development and data processing Experience with data preprocessing, ETL workflows , and feature engineering for AI applications Strong knowledge of containerization tools like Docker and orchestration platforms like Kubernetes Solid understanding of CI/CD pipelines for continuous deployment and integration of AI solutions Experience working with large datasets for structured and unstructured AI applications Deep experience in model optimization, including hyperparameter tuning and computational efficiency strategies Proven track record of leading POCs/pilots and scaling them to production-grade deployments Preferred Skills Familiarity with Reinforcement Learning from Human Feedback (RLHF) . Experience with REACT (Retrieve, Extract, Adapt, Construct, Think) frameworks. Strong understanding of orchestration for large-scale production environments. Key Attributes Strong technical leadership and mentorship abilities. Excellent communication and stakeholder management skills. Strategic thinking with the ability to architect scalable and future-ready AI systems. Passion for solving business challenges using state-of-the-art AI techniques. Commitment to staying updated with the latest advancements in AI/ML technologies. Why Join Us? Lead the development of cutting-edge Generative AI solutions for real-world applications. Be part of a collaborative, innovative, and technology-driven team. Opportunity to work with advanced AI/ML tools and frameworks. Drive innovation through technical leadership , mentorship, and solution evangelization. Continuous professional growth with access to the latest AI/ML technologies and frameworks.

Additional Career Level Description: Knowledge and application: Seasoned, experienced professional; has complete knowledge and understanding of area of specialization. Uses evaluation, judgment, and interpretation to select right course of action. Problem solving: Works on problems of diverse scope where analysis of information requires evaluation of identifiable factors. Resolves and assesses a wide range of issues in creative ways and suggests variations in approach. Interaction: Enhances relationships and networks with senior internal/external partners who are not familiar with the subject matter often requiring persuasion. Works with others outside of own area of expertise, with the ability to adapt style to differing audiences and often advises others on difficult matters. Impact: Impacts short to medium term goals through personal effort or influence over team members. Accountability: Accountable for own targets with work reviewed at critical points. Work is done independently and is reviewed at critical points.

Job Overview As a Senior Dev ops Engineer, Machine Learning (ML) Operations in the Technology & Engineering division, you will be responsible for enabling PitchBook's Machine Learning teams and practitioners by providing tools that optimize all aspects of the Machine Learning Development Life Cycle (MLDLC). Your work will support projects in a variety of domains, including Generative AI (GenAI), Large Language Models (LLMs), Natural Language Processing (NLP), Classification, and Regression. Team Overview Your teams goal will be to reduce friction and time-to-business-value for teams building Artificial Intelligence (AI) solutions at PitchBook. You will be essential in helping to build exceptional AI solutions relied upon and used by thousands of PitchBook customers every day. You will work with PitchBook professionals around the world with the collective goal of delighting our customers and growing our business. While demonstrating a growth mindset, you will be expected to continuously develop your expertise in a way that enhances PitchBooks AI capabilities in a scalable and repeatable manner. You will be able to solve various common challenges faced in the MLDLC while providing technical guidance to less experienced peers. Outline of Duties and Responsibilities Serve as a force multiplier for development teams by creating golden paths that remove roadblocks and improve ideation and innovation. Collaborate with other engineers, product managers, and internal stakeholders in an Agile environment. Provide mentorship , technical guidance , and perform code reviews for team members. Design and deliver on projects end-to-end with little to no guidance. Provide support to teams building and deploying AI applications by addressing common pain points in the MLDLC. Learn constantly and be passionate about discovering new tools, technologies, libraries, and frameworks (commercial and open source), that can be leveraged to improve PitchBooks AI capabilities. Support the vision and values of the company through role modeling and encouraging desired behaviors. Participate in various cross-functional company initiatives and projects as requested. Contribute to strategic planning in a way that ensures the team is building exceptional products that bring real business value. Evaluate frameworks, vendors, and tools that can be used to optimize processes and costs with minimal guidance. Experience, Skills and Qualifications Degree in Computer Science, Information Systems, Machine Learning, or a similar field preferred (or commensurate experience). 5+ years of experience in hands-on development of Machine Learning algorithms. 5+ years of experience in hands-on deployment of Machine Learning services 5+ years of experience supporting the entire MLDLC, including post-deployment operations such as monitoring and maintenance 5+ years of experience with Amazon Web Services (AWS) and/or Google Cloud Platform (GCP) Experience with at least 80%: PyTorch, Tensorflow, LangChain, scikit-learn, Redis, Elasticsearch, Amazon SageMaker, Google Vertex AI, Weights & Biases, FastAPI, Prometheus, Grafana, Apache Kafka, Apache Airflow, MLflow, KubeFlow Ability to break large, complex problems into well-defined steps, ensuring iterative development and continuous improvement Experience in cloud-native delivery, with a deep practical understanding of containerization technologies such as Kubernetes and Docker, and the ability to manage these across different regions. Proficiency in Git Ops and creation/management of CI/CD pipelines. Demonstrated experience building and using SQL/NoSQL databases. Demonstrated experience with Python (Java is a plus) and other relevant programming languages and tools. Excellent problem-solving skills with a focus on innovation, efficiency, and scalability in a global context. Strong communication and collaboration skills, with the ability to engage effectively with internal customers across various cultures and regions. Ability to be a team player who can also work independently. Experience working across multiple development teams is a plus. Morningstar is an equal opportunity employer.

Exciting opportunity for an ML Platform Specialist to join a leading technology-driven firm. You will be designing, deploying, and maintaining scalable machine learning infrastructure with a strong focus on Databricks, model lifecycle, and MLOps practices. Location: Gurugram (Hybrid) Your Future Employer Our client is a leading digital transformation partner driving innovation across industries. With a strong focus on data-driven solutions and cutting-edge technologies, they are committed to fostering a collaborative and growth-focused environment. Responsibilities Designing and implementing scalable ML infrastructure on Databricks Lakehouse Building CI/CD pipelines and workflows for machine learning lifecycle Managing model monitoring, versioning, and registry using MLflow and Databricks Collaborating with cross-functional teams to optimize machine learning workflows Driving continuous improvement in MLOps and automation strategies Requirements Bachelors or Masters in Computer Science, ML, Data Engineering, or related field 3-5 years of experience in MLOps, with strong expertise in Databricks and Azure ML Proficient in Python, PySpark, MLflow, Delta Lake, and Databricks Feature Store Hands-on experience with cloud platforms (Azure/AWS/GCP), CI/CD, Git Knowledge of Terraform, Kubernetes, Azure DevOps, and distributed computing is a plus Whats in it for you Competitive compensation with performance-driven growth opportunities Work on cutting-edge MLOps infrastructure and enterprise-scale ML solutions Collaborative, diverse, and innovation-driven work culture Continuous learning, upskilling, and career development support

Job role & responsibilities:- Collaborate with different teams to propose AI solutions on different use cases across the insurance value chain, with a focus on AIops and MLOps Research, build, and deploy AI models as part of the broader AI team, leveraging AIops and MLOps practices for efficient model management Contribute to our DevOps practices using OpenShift or Azure ML DevOps Technical Skills, Experience & Qualification required:- Expertise is required in the following fields: 6-9 years of progressive experience in AI and ML, with a focus on AIops and MLOps Experience in ML Flow or Cube Flow or Airflow, ML Ops, more in to production deployment Experience in deploying and managing AI models in production environments using Azure ML DevOps or OpenShift Implementation of at least 5 AI projects, preferably with experience in AIops and MLOps Experience with Azure, OpenShift, MLFlow DevOps for model deployment, monitoring, and management Setting up CI/CD pipelines using Azure DevOps, Jenkins, etc. Hands-on experience with Generative AI tech LLMs, RAG, Prompt Engineering Broad understanding of machine learning algorithms and techniques, including LLMs/SLMs, CNNs) RNNs, transformers, and attention mechanisms Immediate Joiners will be preferred

DO NOT APPLY WITHOUT UNDERSTANDING THE ENTIRE JOB DESCRIPTION We are Hiring a Senior Data Scientist for a Leading Fortune 500 Manufacturing Brand Location: Mumbai Experience: Minimum 8 years in Data Science - AI/ML Education: M.E / M.Tech in Computer Science/ IT/ Data Science/ with persuasion of PhD Job Description: - Sr. Data Scientist will be required to build hypothesis, research, prototype, design, develop, and help implement enterprise level ML/ AI/Gen AI models for projects to transform and improve companys business results and competitive position & ensure alignment to the overall digital, data & AI strategy and current and future business objectives. Contribution to Data & AI Platform and Products Work within Digital Data AI Team in development of data science & AI capabilities and features to support delivery on defined objectives around platform aspects Design, implement, and evolve robust, secure and quality solutions that operate for the business ecosystem. Research, design & development of High-Quality Data, AI and analytical systems Define exploratory data analysis (EDA) keeping in line with problem necessities Ensure the development of quality procedures and standards products and supervising tests. Work on various data correction problems such as data cleansing, sourcing and integrating from multiple platforms to make the good data available for data analysis , data science & AI development Provide and help deliver the Solutions Hands on contribution to provide solutions and POCs , working in cross- functional or agile teams to develop and deliver significant aspects of the models and systems. Lead and mentor junior data scientists and ensure availability of necessary data and analysis as needed by business requirement and use cases. Conduct diagnostic across existing data and make future state recommendations in regular intervals. Collaborate with Business Analyst, Data scientists, Data Engineers and Data Analysts to ensure understanding and alignment between business needs and technical implementation. Monitor performance of existing solutions across use cases to identify and drive optimization. Oversee and Research, develop and analyse NLP, Gen AI , computer vision algorithms in Various use cases. Ensure model robustness, model generalization, accuracy, testability, and efficiency. Write product or system development code. Contributing via understanding of machine learning techniques and algorithms, including clustering, anomaly detection, optimization, neural network etc Responsible for deploying AI/ML models (ML Ops) in standalone and cloud based systems and services. Support Projects and Initiatives Help in Collaboration with business unit heads and corporate functions to identify and assist in providing data analysis support across different projects Identify data from legacy systems, to build new solutions based on requirement Provide necessary technical support in new and ongoing digital initiatives to ensure seamless data solutioning. Requisite Experience Essential Minimum 10 Yrs with of experience in data analytics & data science, building, and maintaining various ML models. Good know how of emerging small and larger models 3 to 5 + years of experience in each of the Data Science specialization like NLP, Demand Forecasting, ML Ops Should be able to present portfolio of data science work or use cases Preferred Sound understanding of Data analysis to support the preparatory work Experience working in the agile Environment. Know how/ Familiarity in all aspects of MLOps (source control, continuous integration, deployments, etc.) Experience/ Exposure with Cloud data services like AWS or Azure Special Skills Required Functional: Excellent understanding of machine learning techniques and algorithms, including clustering, anomaly detection, optimization, neural network etc. Strong hands-on coding skills in Python, processing large-scale data set and developing machine learning models. Experience programming in Python, R, and SQL Expertise in developing ML models and deployment of the same Hands on working on developing NLP models using transformers and computer vision. Know-how of deploying AI/ML models (ML Ops) in standalone and cloud-based systems and services. Comfortable working with DevOps: Jenkins, Bitbucket, CI/CD SQL Server experience required Understanding of, dimensional data modelling, structured query language (SQL) skills, data warehouse and reporting techniques Data Governance & Ethics Leadership: Strong analytical skills, ability to ask right questions, analyse data and draw conclusion by making appropriate assumptions, to solve and model complex business requirements Ability to lead team of junior data scientists, get into the details of the problem and ability to code the solution hands on as and when needed Planning & Organizing Present complex data analysis in consumable way and Engage the stakeholders Ability to collaborate with different teams and clearly communicate solutions to both technical and non-technical team members Team player

Skilled in MLOps with basic LLMOps knowledge, strong Python and frameworks like FastAPI/Flask, DB integration, SQL, ORM, Alembic; experienced in code reviews and backend design from requirements. Mail: kowsalya.k@srsinfoway.com

3+ years of work experience in Python programming for AI/ML, deep learning, and Generative AI model development Proficiency in TensorFlow/PyTorch, Hugging Face Transformers and Langchain libraries Hands-on experience with NLP, LLM prompt design and fine-tuning, embeddings, vector databases and agentic frameworks Strong understanding of ML algorithms, probability and optimization techniques 6+ years of experience in deploying models with Docker, Kubernetes, and cloud services (AWS Bedrock, SageMaker, GCP Vertex AI) through APIs, and using MLOps and CI/CD pipelines Familiarity with retrieval-augmented generation (RAG), cache-augmented generation (CAG), retrieval-integrated generation (RIG), low-rank adaptation (LoRA) fine-tuning Ability to write scalable, production-ready ML code and optimized model inference Experience with developing ML pipelines for text classification, summarization and chat agents Prior experience with SQL and noSQL databases, and Snowflake/Databricks

Roles & Responsibilities: Design, develop, and optimize computer vision models for tasks like object detection, image segmentation, and video analysis. Implement machine learning algorithms and techniques using Python frameworks (e.g., TensorFlow, PyTorch, OpenCV). Preprocess and augment image/video datasets for model training and testing. Perform exploratory data analysis to extract insights and refine data pipelines. Collaborate with DevOps and engineering teams to deploy machine learning models into production environments. Set up monitoring and performance evaluation for deployed models. Contribute to CI/CD pipelines for AI workflows. Work closely with cross-functional teams, including data scientists, engineers, and product managers, to align on project goals. Document processes, models, and experiments effectively. Requirements: Proficiency in Python and popular AI/ML libraries (TensorFlow, PyTorch, Scikit-learn, OpenCV). Strong understanding of computer vision concepts such as feature extraction, image classification, and object detection. Hands-on experience in developing and training deep learning models for image and video data. Basic knowledge of MLOps practices, including model deployment (e.g., Docker, Kubernetes) and monitoring. Familiarity with version control systems like Git. Experience with cloud platforms (e.g., AWS, GCP, Azure) for AI/ML workloads.

Role & responsibilities Industry knowledge - Knows basics of machine learning, is aware of cloud services, Azure services, has a deep understanding of coding practices, knows how to guide teams on debugging the issues, can connect the dots to arrivie at a solution and is very good at presentation of the ideas, thoughts and solutions. Technical knowledge - has expertise in cloud technologies, specifically MS Azure, and services with hands on coding to Expertise in Object Oriented Python Programming with 4 -5 years experience. DevOps Working knowledge with implementation experience - 1 or 2 projects a minimum Hands-On MS Azure Cloud knowledge Understand and take requirements on Operationalization of ML Models from Data Scientist Help team with ML Pipelines from creation to execution List Azure services required for deployment, Azure Data bricks and Azure DevOps Setup Assist team to coding standards (flake8 etc) Guide team to debug on issues with pipeline failures Engage with Business / Stakeholders with status update on progress of development and issue fix Automation, Technology and Process Improvement for the deployed projects Setup Standards related to Coding, Pipelines and Documentation Adhere to KPI / SLA for Pipeline Run, Execution Research on new topics, services and enhancements in Cloud Technologies Responsible for successful delivery of MLOps solutions and services in client consulting environments; Define key business problems to be solved; formulate high level solution approaches and identify data to solve those problems, develop, analyze/draw conclusions and present to client. Assist clients with operationalization metrics to track performance of ML Models Agile trained to manage team effort and track through JIRA High Impact Communication - Assesses the target audience need, prepares and practices a logical flow, answers audience questions appropriately and sticks to timeline. Preferred candidate profile Education and Experience: Overall, 6 to 8 years of experience in Data driven software engineering with 3-5 years of experience designing, building and deploying enterprise AI or ML applications with at least 2 years of experience implementing full lifecycle ML automation using MLOps(scalable development to deployment of complex data science workflows) Bachelors or Master’s degree in Computer Science Engineering or equivalent Domain experience in Retail, CPG and Logistics etc. Azure Certified – DP100, AZ/AI900 Domain / Technical / Tools Knowledge : Object oriented programming, coding standards, architecture & design patterns, Config management, Package Management, Logging, documentation Experience in Test Driven Development and experience in using Pytest frameworks, git version control, Rest APIs Azure ML best practices in environment management, run time configurations (Azure ML & Databricks clusters), alerts. Experience designing and implementing ML Systems & pipelines, MLOps practices and tools such a MLFlow, Kubernetes, etc. Exposure to event driven orchestration, Online Model deployment Contribute towards establishing best practices in MLOps Systems development Proficiency with data analysis tools (e.g., SQL, R & Python) High level understanding of database concepts/reporting & Data Science concepts Hands on experience in working with client IT/Business teams in gathering business requirement and converting into requirement for development team Experience in managing client relationship and developing business cases for opportunities Azure AZ-900 Certification with Azure Architecture understanding is a plus

We are seeking high-energy, driven, and innovative Data Scientists to join our Data Science Practice to develop new, specialized capabilities for Axtria, and to accelerate the company's growth by supporting our clients commercial & clinical strategies. Job Responsibilities Be an Individual Contributor to the Data Science team and solve real-world problems using cutting-edge capabilities and emerging technologies. Help clients translate the business use cases they are trying to crack into data science solutions . Provide genuine assistance to users by advising them on how to leverage Dataiku DSS to implement data science projects, from design to production. Data Source Configuration, Maintenance, Document and maintain work-instructions . Deep working on machine learning frameworks such as TensorFlow, Caffe, Keras, SparkML. Expert knowledge in Statistical and Probabilistic methods such as SVM, Decision-Trees, Clustering. Expert knowledge of python data-science and math packages such as NumPy, Pandas, Sklearn. Proficiency in object-oriented languages (Java and/or Kotlin), Python and common machine learning frameworks (TensorFlow, NLTK, Stanford NLP, Ling Pipe etc.). Education Bachelor Equivalent - Engineering Master's Equivalent - Engineering Work Experience Data Scientist: 3-5 years of relevant experience in advanced statistical and mathematical models and predictive modeling using Python. Experience in the data science space prior relevant experience in Artificial intelligence and Machine Learning algorithms for developing scalable models, supervised and unsupervised techniques like NLP and deep Learning Algorithms. Ability to build scalable models using Python, R-Studio, R Shiny, PySpark, Keras, and TensorFlow. Experience in delivering data science projects leveraging cloud infrastructure. Familiarity with cloud technology such as AWS / Azure and knowledge of AWS tools such as S3, EMR, EC2, Redshift, and Glue; viz tools like Tableau and Power BI. Relevant experience in Feature Engineering, Feature Selection, and Model Validation on Big Data. Knowledge of self-service analytics platforms such as Dataiku/ KNIME/ Alteryx will be an added advantage. ML Ops Engineering: 3-5 years of experience with MLOps Frameworks like Kubeflow, MLFlow, Data Robot, Airflow, etc., experience with Docker and Kubernetes, OpenShift. Prior experience in end-to-end automated ecosystems including, but not limited to, building data pipelines, developing & deploying scalable models, orchestration, scheduling, automation, and ML operations. Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS, MS Azure, or GCP). Programming languages like Python, Go, Ruby, or Bash, a good understanding of Linux, knowledge of frameworks such as Keras, PyTorch, TensorFlow, etc. Ability to understand tools used by data scientists and experience with software development and test automation. Good understanding of advanced AI/ML algorithms & their applications. Gen AI: Minimum of 4-6 years develop, test, and deploy Python based applications on Azure/AWS platforms. Must have basic knowledge on concepts of Generative AI / LLMs / GPT . Deep understanding of architecture and work experience on Web Technologies. Python, SQL hands-on experience . Expertise in any popular python web frameworks e.g. flask, Django etc. Familiarity with frontend technologies like HTML, JavaScript, REACT. Be an Individual Contributor in the Analytics and Development team and solve real-world problems using cutting-edge capabilities and emerging technologies based on LLM/GenAI/GPT. Can interact with client on GenAI related capabilities and use cases.

We are seeking high-energy, driven, and innovative Data Scientists to join our Data Science Practice to develop new, specialized capabilities for Axtria, and to accelerate the company's growth by supporting our clients commercial & clinical strategies. Job Responsibilities Be an Individual Contributor to the Data Science team and solve real-world problems using cutting-edge capabilities and emerging technologies. Help clients translate the business use cases they are trying to crack into data science solutions . Provide genuine assistance to users by advising them on how to leverage Dataiku DSS to implement data science projects, from design to production. Data Source Configuration, Maintenance, Document and maintain work-instructions . Deep working on machine learning frameworks such as TensorFlow, Caffe, Keras, SparkML. Expert knowledge in Statistical and Probabilistic methods such as SVM, Decision-Trees, Clustering. Expert knowledge of python data-science and math packages such as NumPy, Pandas, Sklearn. Proficiency in object-oriented languages (Java and/or Kotlin), Python and common machine learning frameworks (TensorFlow, NLTK, Stanford NLP, Ling Pipe etc.). Education Bachelor Equivalent - Engineering Master's Equivalent - Engineering Work Experience Data Scientist: 3-5 years of relevant experience in advanced statistical and mathematical models and predictive modeling using Python. Experience in the data science space prior relevant experience in Artificial intelligence and Machine Learning algorithms for developing scalable models, supervised and unsupervised techniques like NLP and deep Learning Algorithms. Ability to build scalable models using Python, R-Studio, R Shiny, PySpark, Keras, and TensorFlow. Experience in delivering data science projects leveraging cloud infrastructure. Familiarity with cloud technology such as AWS / Azure and knowledge of AWS tools such as S3, EMR, EC2, Redshift, and Glue; viz tools like Tableau and Power BI. Relevant experience in Feature Engineering, Feature Selection, and Model Validation on Big Data. Knowledge of self-service analytics platforms such as Dataiku/ KNIME/ Alteryx will be an added advantage. ML Ops Engineering: 3-5 years of experience with MLOps Frameworks like Kubeflow, MLFlow, Data Robot, Airflow, etc., experience with Docker and Kubernetes, OpenShift. Prior experience in end-to-end automated ecosystems including, but not limited to, building data pipelines, developing & deploying scalable models, orchestration, scheduling, automation, and ML operations. Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS, MS Azure, or GCP). Programming languages like Python, Go, Ruby, or Bash, a good understanding of Linux, knowledge of frameworks such as Keras, PyTorch, TensorFlow, etc. Ability to understand tools used by data scientists and experience with software development and test automation. Good understanding of advanced AI/ML algorithms & their applications. Gen AI: Minimum of 4-6 years develop, test, and deploy Python based applications on Azure/AWS platforms. Must have basic knowledge on concepts of Generative AI / LLMs / GPT . Deep understanding of architecture and work experience on Web Technologies. Python, SQL hands-on experience . Expertise in any popular python web frameworks e.g. flask, Django etc. Familiarity with frontend technologies like HTML, JavaScript, REACT. Be an Individual Contributor in the Analytics and Development team and solve real-world problems using cutting-edge capabilities and emerging technologies based on LLM/GenAI/GPT. Can interact with client on GenAI related capabilities and use cases.

We are seeking a highly experienced and strategic Senior Manager Applied Science to lead and scale our applied research and machine learning initiatives. This role involves working at the intersection of cutting-edge AI/ML technologies and real-world business challenges to drive measurable impact. Key Responsibilities: Lead a team of applied scientists and machine learning engineers to develop scalable AI/ML models. Define and drive the scientific vision, strategy, and roadmap aligned with business goals. Collaborate cross-functionally with product, engineering, and analytics teams. Stay ahead of emerging technologies and apply them to solve complex business problems. Publish research, file patents, or present findings in internal/external forums as needed. Requirements: 10+ years of experience in applied science, machine learning, or data science roles. Strong background in statistics, optimization, deep learning, NLP, or related fields. Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. Proven experience in leading high-performance technical teams. Advanced degree (Ph.D. or Master’s) in Computer Science, Mathematics, Statistics, or a related field. Excellent communication and leadership skills. Preferred Qualifications: Experience in a product-led tech company or research lab environment. Track record of deploying ML models in production. Publications or patents in relevant areas.