12 Ml Operations Jobs

Join our Chief Data and Analytics Office to develop enterprise-scale, cutting-edge platforms for Data Management & Analytics and AI/ML Operations utilized firm-wide by the JPMC workforce for Artificial Intelligence (including generative AI)/Machine Learning (AI/ML) development and Data Management. As a Product Director - Data Governance in the Chief Data & Analytics Organization at JP Morgan Chase, you will be responsible for leading the development of product strategies and major initiatives focused on Data Management governance frameworks, policies, and procedures. Your role is crucial in ensuring the ethical and compliant use of AI & Data Management technologies across the organization. You will be involved in integrating Data Management technology into the company's infrastructure while adhering to sustainable best practices aligned with JPMC technology, operational risk, and relevant regulations. Collaboration with cross-functional teams, including the firm-wide Chief Data Officer, data scientists, engineers, legal, compliance, and business units, will be essential to promote AI & Data Management governance initiatives meeting regulatory requirements and industry standards. Additionally, overseeing the local team to ensure effective delivery of risk and control measures, action plans, control processes, and readiness for audits and regulatory examinations is part of your responsibilities. Responsibilities: - Drive product strategy by designing user-friendly products incorporating comprehensive AI governance frameworks, policies, and procedures to ensure the ethical use of AI technologies. - Ensure compliance with relevant AI & Data Management regulations, standards, and guidelines such as GDPR, CCPA, and emerging regulations. - Identify, assess, and mitigate risks related to AI & Data Management technologies including data quality, privacy, bias, transparency, and accountability. - Lead the entire product life cycle from planning to execution, continuously adapting, developing new products and methodologies to achieve business targets. - Coach and mentor the product team on best practices, enabling them to effectively deliver on objectives. - Own product performance and drive enhancements to meet business objectives. - Monitor market trends, conduct competitive analysis, and identify opportunities for product differentiation. - Collaborate with cross-functional teams to align product strategy with business objectives. Required qualifications, capabilities, and skills: - 10+ years of experience delivering products, projects, or technology applications within the AI & Data Governance area. - Extensive knowledge of the product development life cycle, technical design, data analytics, and cloud usage. - Proven ability to influence key product life cycle activities and drive change within organizations. - Experience in executive-level product management within a large organization. - Strong strategic thinking and product development skills. - Excellent communication, leadership, and problem-solving skills. Preferred qualifications, capabilities, and skills: - Recognized thought leader in a related field. - Familiarity with the centralized Chief Data and Analytics Office operations. - Advanced degree in a related field (e.g., Computer Science, Business Administration). - Demonstrated success in leading cross-functional teams and driving innovation.,

Key critical skills required for this role include: Ability to work dedicated shifts in the range of 12 Noon IST to 12 AM IST. Minimum 6 Years experience in Data Science Domain (Analytics and Reporting). Ability to write and study and correct Python, SQL code is mandatory. Ability to work with Big Data. Ability to articulate a Value Proposition and Enhancement opportunity. Visualization Tools exposure like Power BI, Tableau. AWS Services exposure. Machine Learning Operations Py Spark exposure You may be assessed on key essential skills relevant to succeed in role, such as strong knowledge on Python, SQL, Big Data, Power BI, Tableau strategic thinking as well as job-specific technical skills.

Experience in AWS Cloud Mandatory along with ML Ops

As an Engineer 4, Machine Learning at Comcast, you will be a vital part of our international engineering team, dedicated to transforming innovative ideas into cutting-edge products and solutions that cater to the needs of our customers. At Comcast, we provide an environment that fosters innovation and encourages you to bring your authentic self to work. Your contributions will be valued and recognized, allowing you to showcase your skills and expertise while achieving personal and professional growth. In this senior-level data science role, you will be responsible for leading the design, development, and implementation of advanced time series forecasting models. Your primary focus will be on utilizing various techniques such as ARIMA, Exponential Smoothing, Seasonal Decomposition, Prophet, LSTM networks, and other cutting-edge algorithms to predict future trends based on historical data. Your analytical skills will be put to the test as you thoroughly analyze large volumes of time series data to identify patterns, trends, seasonality, and anomalies. Key Responsibilities: - Design and develop complex time series forecasting models based on business needs - Analyze large volumes of time series data to extract valuable insights - Fine-tune LLM models and prototype solutions for ML/LLM operations - Lead a team of data scientists and engineers working on time series forecasting projects - Provide technical guidance, mentorship, and knowledge transfer to team members To excel in this role, you should possess a Bachelor's Degree and have 7-10 years of relevant work experience. While having a Bachelor's Degree is preferred, Comcast also values applicants with a combination of coursework and experience or extensive related professional experience. At Comcast, we are committed to supporting our employees both personally and professionally. We offer comprehensive benefits that cater to your physical, financial, and emotional well-being, ensuring that you have the support you need to thrive in your career and personal life. If you are a results-driven, adaptable, and inventive individual who thrives in a collaborative team environment, we invite you to fast-forward your career with us at Comcast in Chennai, India.,

As an Engineer 4 specializing in Machine Learning at Comcast, you will be a crucial part of our international engineering team. Your primary responsibility will involve designing, developing, and implementing advanced time series forecasting models to predict future trends based on historical data. Your expertise will be pivotal in creating cutting-edge solutions that drive innovation and enhance customer experiences. Key Responsibilities: - Lead the design and development of complex time series forecasting models utilizing various techniques such as ARIMA, Exponential Smoothing, Seasonal Decomposition, Prophet, LSTM networks, and other advanced algorithms tailored to meet business requirements. - Conduct in-depth analysis of large volumes of time series data to identify patterns, trends, seasonality, and anomalies. - Fine-tune LLM models and focus on time series models to optimize performance. - Prototype and create solutions for ML/LLM operations and Applied AI. - Supervise a team of data scientists and engineers dedicated to time series forecasting projects. - Offer technical guidance, mentorship, and knowledge sharing on time series analysis techniques and best practices to team members. At Comcast, we value employees who embody the following principles: - Embrace our Operating Principles as guidelines for your work. - Prioritize the customer experience by consistently putting customers first and providing seamless digital options. - Stay informed and enthusiastic about our technology, products, and services to effectively advocate for them. - Foster teamwork and remain open to new ideas to achieve significant milestones together. - Participate actively in the Net Promoter System, contributing to employee and customer feedback to enhance our services. - Drive results, growth, respect inclusion and diversity, and act in the best interests of all stakeholders. Educational Requirement: Bachelor's Degree While a Bachelor's Degree is preferred, Comcast may also consider candidates with a combination of coursework and experience or significant related professional experience. Relevant Work Experience: 7-10 Years Join us at Comcast and be part of our dynamic team dedicated to revolutionizing the media and technology industry. Fast-forward your career with us and contribute to creating exceptional entertainment and online experiences for millions of customers worldwide.,

Role and Designation : ML Ops Engineer Number of Positions : 1 Desired Experience : 3 to 6 years Detailed Job Description: We are seeking an ML Ops Engineer to operationalize and manage the lifecycle of machine learning models, especially those built using Azure and Databricks. You will be key in deploying, monitoring, and optimizing models across multiple business use cases. Key Responsibilities: Maintain, optimize, and scale recommendation engines used in FMCG or consumer goods environments to ensure high availability, accuracy, and low-latency performance across channels. Continuously improve recommendation models using advanced ML techniques such as collaborative filtering, content-based filtering, matrix factorization, and hybrid recommender systems. Enhance and maintain graph-based models , focusing on optimizing graph embeddings (e.g., Node2Vec, GraphSAGE, GNNs) to better capture relationships across products, users, and behaviours. Develop and evolve an enterprise-grade knowledge graph to unify structured and unstructured data from multiple sourcesenabling context-aware personalization, semantic search, and relationship-driven recommendations. Ensure explainability, reproducibility, and scalability of recommendation workflows through proper model documentation, ML versioning, and MLOps best practices. Collaborate with product, marketing, and engineering teams to integrate recommendations into consumer-facing applications, personalization engines, or B2B retail platforms. Required Skills: Programming Languages : Python (advanced), Scala (beneficial) Big Data Technologies : Apache Spark, PySpark, Delta Lake Data Formats & Storage : Parquet, optimizing Parquet schema/compression, Azure Blob Storage Cloud Platforms : Azure (Databricks, ADF, Storage, Key Vault) Graph Technologies : Graph algorithms, embeddings, NetworkX, GraphFrames ML/AI : Machine learning basics, recommendation algorithms, embeddings Monitoring & Logging : Grafana, Application Insights, Log Analytics Advanced knowledge in retrieval augmented generation systems Expertise in vector databases and embedding strategies Proficiency in context retrieval algorithms Experience designing evaluation frameworks for AI systems Strong background in segmented execution frameworks Skills in distributed computing solutions for AI workloads Knowledge of intelligent task routing systems Ability to create orchestration layers for AI workflows Experience integrating AI capabilities into enterprise applications Proficiency in API design and implementation for AI systems Knowledge of CI/CD pipelines for AI components Nice to Have Skills in monitoring and observability solutions Experience with AI-powered forecasting and optimization Expertise in intelligent document processing Knowledge of AI assistant development Proficiency in advanced analytics and trend identification

Role & responsibilities University Degree in Computer Science, Information Technology, or related field 5+ years of experience in the Machine Learning Operations role Design the data pipelines and engineering infrastructure to support our clients enterprise machine learning systems at scale Take offline models data scientists build and turn them into a real machine learning production system Develop and deploy scalable tools and services for our clients to handle machine learning training and inference Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients machine learning systems Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc. Support model development, with an emphasis on auditability, versioning, and data security Facilitate the development and deployment of proof-of-concept machine learning systems Communicate with clients to build requirements and track progress Strong analytic skills related to working with structured, semi structured and unstructured datasets Advanced Machine learning techniques: Decision Trees, Random Forest, Boosting Algorithm, Neural Networks, Deep Learning, Support Vector Machines, Clustering, Bayesian Networks, Reinforcement Learning, Feature Reduction

Job Description: Role Overview We are seeking a highly skilled AI Engineer with 8-12 years of experience to lead the development of Generative AI (GenAI) and Machine learning solutions for internal projects. This role requires a self-driven leader with exceptional communication, strategic thinking, and expertise in data analytics and visualization to deliver innovative GenAI tools tailored to internal team needs while driving cross-functional collaboration. Key Responsibilities GenAI Development: Design and develop advanced GenAI models (e.g., LLMs, DALL E models) and AI Agents to automate internal tasks and workflows. Exposure to LLMs: Utilize Azure Open AI APIs, experience on models like GPT4o, O3 , llama3 Enhance the existing R AG based application: In depth understanding of stages of RAG - chunking, retrieval etc. Cloud Deployment: Deploy and scale GenAI solutions on Azure Cloud services (e.g., Azure Function App) for optimal performance. In depth understanding of ML models like linear regression, random forest, decision trees. In depth understanding on clustering and supervised models. AI Agent Development: Build AI agents using frameworks like LangChain to streamline internal processes and boost efficiency. Data Analytics : Perform advanced data analytics to preprocess datasets, evaluate model performance, and derive actionable insights for GenAI solutions. Data Visualization: Create compelling visualizations (e.g., dashboards, charts) to communicate model outputs, performance metrics, and business insights to stakeholders. Stakeholder Collaboration: Partner with departments to gather requirements, align on goals, and present technical solutions and insights effectively to non-technical stakeholders. Model Optimization: Fine-tune GenAI models for efficiency and accuracy using techniques like prompt engineering, quantization, and RAG (Retrieval-Augmented Generation). LLMOps Best Practices: Implement GenAI-specific MLOps, including CI/CD pipelines (Git, Azure DevOps) Leadership: Guide cross-functional teams, mentor junior engineers, and drive project execution with strategic vision and ownership. Helicopters, strategic Thinking: Develop innovative GenAI strategies to address business challenges, leveraging data insights to align solutions with organizational goals. Self-Driven Execution: Independently lead projects to completion with minimal supervision, proactively resolving challenges and seeking collaboration when needed. Continuous Learning: Stay ahead of GenAI, analytics, and visualization advancements, self-learning new techniques to enhance project outcomes. Required Skills & Experience : 8-12 years in AI/ML development, with at least 4 years focused on Generative AI and AI agent frameworks. Education: BTech/BE in Computer Science, Engineering, or equivalent (Masters or PhD in AI/ML is a plus). Programming: Expert-level Python proficiency, with deep expertise in GenAI libraries (e.g., LangChain, Hugging Face Transformers, PyTorch, Open AI SDK) and data analytics libraries (e.g., Pandas, NumPy), sk-learn. Mac Data Analytics: Strong experience in data preprocessing, statistical analysis, and model evaluation to support GenAI development and business insights. Data Visualization: Proficiency in visualization tools (e.g., Matplotlib, Seaborn, Plotly, Power BI, or Tableau) to create dashboards and reports for stakeholders. Azure Cloud Expertise: Strong experience with Azure Cloud services (e.g., Azure Function App, Azure ML, serverless) for model training and deployment. GenAI Methodologies : Deep expertise in LLMs, AI agent frameworks, prompt engineering, and RAG for internal workflow automation. Deployment : Proficiency in Docker, Kubernetes, and CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for production-grade GenAI systems. LLMOps: Expertise in GenAI MLOps, including experiment tracking (e.g., Weights & Biases), automated evaluation metrics (e.g., BLEU, ROUGE), and monitoring. Communication: Exceptional verbal and written skills to articulate complex GenAI concepts, analytics, and visualizations to technical and non-technical stakeholders. Strategic Thinking: Ability to align AI solutions with business objectives, using data-driven insights to anticipate challenges and propose long-term strategies. Problem-Solving : Strong analytical skills with a proactive, self-starter mindset to independently resolve complex issues. Collaboration: Collaborative mindset to work effectively across departments and engage colleagues for solutions when needed. Preferred Skills Experience deploying GenAI models in production environments, preferably on Azure Familiarity with multi-agent systems, reinforcement learning, or distributed training (e.g., DeepSpeek). Knowledge of DevOps practices, including Git, CI/CD, and infrastructure-as-code. Advanced data analytics techniques (e.g., time-series analysis, A/B testing) for GenAI applications. Experience with interactive visualization frameworks (e.g., Dash, Streamlit) for real-time dashboards. Contributions to GenAI or data analytics open-source projects or publications in NLP, generative modeling, or data science

Location: Bangalore/Noida/Pune/Gurgaon Education: B.E. / B. Tech / M.E. / M. Tech / MCA Job Responsibilities: Model Deployment and Management: Drive ML prototypes into production ensuring seamless deployment and management on cloud at scale. Monitor real-time performance of deployed models, analyze data, and proactively address performance issues. Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability. Collaboration and Integration: Collaborate with DevOps engineers to manage cloud compute resources for ML model deployment and performance optimization. Work closely with ML scientists, software engineers, data engineers, and other stakeholders to implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, and automated deployment. Innovation and Continuous Improvement: Stay updated with the latest advancements in MLOps technologies and recommend new tools and techniques. Contribute to the continuous improvement of team processes and workflows. Share knowledge and expertise to promote a collaborative learning environment. Development and Documentation: Build software to run and support machine-learning models. Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes. Participate in fast iteration cycles and adapt to evolving project requirements. Business Solutions and Strategy: Propose solutions and strategies to business challenges. Collaborate with Data Science team, Front End Developers, DBA, and DevOps teams to shape architecture and detailed designs. Mentorship: Conduct code reviews and mentor junior team members. Foster strong interpersonal skills, excellent communication skills, and collaboration skills within the team. Mandatory Skills: Programming Languages: Proficiency in Python (3.x) and SQL. ML Frameworks and Libraries: Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture. Databases: Proficiency in SQL and NoSQL databases. Mathematics and Algorithms: In-depth knowledge of mathematics, statistics, and algorithms. ML Modules and REST API: Proficient with ML modules and REST API. Version Control: Hands-on experience with version control applications (GIT). Model Deployment and Monitoring: Experience with model deployment and monitoring. Data Processing: Ability to turn unstructured data into useful information (e.g., auto-tagging images, text-to-speech conversions). Problem-Solving: Analytically agile with strong problem-solving capabilities. Learning Agility: Quick to learn new concepts and eager to explore and build new features. Qualifications: Education: Bachelors or Master’s degree in Computer Science, Data Science, or a related field. Experience: Minimum of 6 years of hands-on experience in MLOps, deploying and managing machine learning models in production environments, preferably in cloud-based environments. Role & responsibilities Preferred candidate profile

**Key Responsibilities** People management - Lead a team of software engineers, DS, DE, MLE, in the design, development, and delivery of software solutions. Program management - Strong program leader that has run program management functions to efficiently deliver ML projects to production and manage its operations. Work with Business stakeholders & customers in the Retail Business domain to execute the product vision using the power of AI/ML. Scope out the business requirements by performing necessary data-driven statistical analysis. Set goals and, objectives using proper business metrics and constraints. Conduct exploratory analysis on large volumes of data, understand the statistical shape, and use the right visuals to drive & present the analysis. Analyse and extract relevant information from large amounts of data and derive useful insights on a big-data scale. Create labelling manuals and work with labellers to manage ground truth data and perform feature engineering as needed. Work with software engineering teams, data engineers and ML operations team (Data Labellers, Auditors) to deliver production systems with your deep learning models. Select the right model, train, validate, test, optimise neural net models and keep improving our image and text processing models. Architecturally optimize the deep learning models for efficient inference, reduce latency, improve throughput, reduce memory footprint without sacrificing model accuracy. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. Create and enhance model monitoring system that could measure data distribution shifts, alert when model performance degrades in production. Streamline ML operations by envisioning human in the loop kind of workflows, collect necessary labels/audit information from these workflows/processes, that can feed into improved training and algorithm development process. Maintain multiple versions of the model and ensure the controlled release of models. Manage and mentor junior data scientists, providing guidance on best practices in data science methodologies and project execution. Lead cross-functional teams in the delivery of data-driven projects, ensuring alignment with business goals and timelines. Collaborate with stakeholders to define project objectives, deliverables, and timelines. **Skills required: ** MS/PhD from reputed institution with a delivery focus. 5+ years of experience in data science, with a proven track record of delivering impactful data-driven solutions. Delivered AI/ML products/features to production. Seen the complete cycle from Scoping & analysis, Data Ops, Modelling, MLOps, Post deployment analysis. Experts in Supervised and Semi-Supervised learning techniques. Hands-on in ML Frameworks - Pytorch or TensorFlow. Hands-on in Deep learning models. Developed and fine-tuned Transformer based models. ( Input output metric, Sampling technique) Deep understanding of Transformers, GNN models and its related math & internals. Exhibit high coding standards and create production quality code with maximum efficiency. Hands-on in Data analysis & Data engineering skills involving Sqls, PySpark etc. Exposure to ML & Data services on the cloud AWS, Azure, GCP Understanding internals of compute hardware - CPU, GPU, TPU is a plus. Can leverage the power of hardware accel to optimize the model execution — PyTorch Glow, cuDNN, is a plus.

Experience in AWS Cloud Mandatory along with ML Ops

Exp - 6- 10 Years Level - Senior Consultant Skill - ML OPS with Gen AI Location - Bengaluru Event on 9th May in Bangalore location Hybrid Mode (2 days WFO in a week) Education - B.Tech/B.E/MS/MBA ML OPs Engineer Required: 6-10 years of Consulting, Data, and Analytics experience Experience in descriptive & predictive analytics, both theoretical and practical knowledge in basic ML algorithms like linear and non-linear regression, linear and non-linear classification, dimensional reduction, anomaly detection, statistical concepts and techniques like theoretical distributions, parametric and non-parametric inference 6+ years of experience implementing & executing data science projects throughout the entire lifecycle: Developing/designing and implementing solutions E2E in production. Strong knowledge of Python or R Programming experience with Node.js, SQL, Java, JavaScript OR PERL Experience in cloud-based data platforms on AWS, GCP and Azure Understanding of multi-tier application architectures Ability to develop, test and maintain programming environments and architectural standards Foundational understanding of application development lifecycle and using tools like ANT, Maven, Gradle and Version control (SVN OR GIT OR BitBucket) Experience with working in an agile development lifecycle and continuous integration processes using tools such as Jenkins Experience in doing deployments for Java, .NET , Angular , Node.js , PHP, Python applications using Jenkins/Bamboo Experience on code quality assessment tools and integration with CI tool Strong logical structuring and problem-solving skills Strong verbal, written and presentation skills Preferred Additional Experience in using Spark either with Scala or Python Experience with different database types like RDS and NoSQL Experience in cloud deployments Knowledge of working in a Linux environment Strong understanding and experience configuring, managing and supporting applications using tools such as OpsWorks, Datadog and CloudWatch on AWS Experience in Docker /Swarm / Kubernetes Experience or exposure to Test Driven Development Experience (Junit / TestNG) Experience on Behavior Driven Development Experience (Cucumber / Selenium) Expertise in any commercial data visualization tool such as Tableau, Qlik, Power BI Experience with real time data movement solutions that use security and encryption protocols while data is in transit