Top Machine Learning Engineer Jobs in Mumbai

Lead design, implementation, and monitoring of control frameworks (CORE); drive automation, AI/ML and analytics for proactive risk identification; own RCSAs and control testing; partner with stakeholders to ensure regulatory compliance, streamline controls, and present findings to senior management.

Lead architecture for Payments Technology within GLCM, guiding technical governance, evaluating and integrating AI and emerging technologies, developing secure production code, and driving architecture standards, reuse, and operational resiliency across product families.

Support daily trading operations from order entry to settlement, reconcile accounts, prepare trading reports, ensure regulatory compliance, drive process improvements, train junior staff, and collaborate with traders and stakeholders to optimize trade services.

Lead architecture for software products across cloud-native domains (AWS), drive technical governance, evaluate and adopt emerging technologies (AI/ML/LLM), produce and review secure production code, guide integration and service design, mentor teams, and influence product and operational decisions.

Lead product strategy and execution for the Enterprise Data Platform's Unstructured Data domain. Define roadmap, manage end-to-end product lifecycle, partner with engineering and UX, drive adoption and go-to-market, measure performance with KPIs, and mentor product managers in a scaled agile, global environment.

Design and implement customer experience data solutions using Qualtrics and analytics platforms. Develop taxonomies, dashboards, ETL processes, and automation; ensure data quality, controls compliance, and stakeholder enablement. Support R&D on emerging technologies and deliver data-driven insights to improve customer journeys.

Perform market-microstructure and algorithmic execution research: develop mathematical models for single-stock and portfolio execution, order placement and routing, build quantitative tools for order-flow analysis, optimize execution performance, and publish white papers to translate research into trading improvements.

Lead analytics to detect and mitigate digital payment fraud across channels. Analyze large datasets, design experiments, build data-driven rules and automated processes, collaborate on fraud-fighting products, present insights to leadership, monitor trends, and measure financial impacts to reduce losses and improve customer experience.

Prepare and review UK and US GAAP regulatory reports and submissions for UK-regulated JP Morgan entities; reconcile and investigate variances; support regulatory projects, trade flow reviews, UAT, and finance technology transformation; assist Legal Entity Controllers and business lines with performance reporting and regulatory queries while driving process improvements and fostering team collaboration.

Lead development and deployment of scalable machine learning and analytics solutions for global map-making. Design process automation, build predictive and statistical models, lead modeling teams, collaborate with researchers, developers, architects, and customers, and work with big-data pipelines and point-cloud/computer-vision methods.

Build data-driven solutions and BI using Python, Power BI, and SQL. Implement DevOps pipelines with GitHub Actions/Azure DevOps, deploy on Azure/AWS, integrate APIs, apply unit testing and automation, and collaborate in Agile/Scrum with global teams. Support solution design, effort estimates, and use ML/DL/LLMs for intelligent features.

Lead technical design and delivery of production AI systems including representation learning, embeddings, vector search, RAG, LLMs and agentic/multimodal solutions. Architect scalable, explainable AI platforms, set best practices, evaluate emerging tech, measure business impact, mentor engineers, and contribute to research and IP.

Design and implement scalable backend services, lead full software lifecycle, write design docs, mentor engineers, collaborate cross-functionally, improve performance and quality, and integrate AI tools to boost development productivity.

Design, develop, and deploy ML models (classification, forecasting, NLP). Build scalable data pipelines, integrate AI solutions into production, and operationalize LLM and generative AI use cases. Improve model performance, ensure monitoring, governance, and lifecycle management while collaborating with Data Ops, Product, and Engineering teams.

Lead design, development, and production deployment of AI systems combining LLMs, agentic architectures, embedding-based retrieval, multimodal models, and deep learning. Build representation and retrieval layers, scale training and inference, implement RAG and agentic workflows, apply vision-language and spatial reasoning, mentor engineers, define KPIs, and translate research into production-quality Python code using PyTorch/TensorFlow and large-scale data tooling.

Seeking a Vice President to develop Machine Learning models and Gen AI use cases in Wealth Management, supporting business lines with predictive analytics and operational model management.

Consult with CPG/FMCG clients on Revenue Growth Management analytics programs, focusing on pricing strategies, promotion analysis, and optimizing revenue growth while leading engagements across diverse global markets.

Develop, calibrate, monitor and automate counterparty risk models and analyses; attribute P&L to risk factors; prototype risk models; build quantitative tools; produce industry trend reports and support stress testing for reporting and event response.

Support the data science team by collecting, cleaning, and analyzing data; perform EDA; develop and test ML models; write SQL; use Python and libraries for preprocessing and feature engineering; build dashboards; document findings; automate reporting; and collaborate cross-functionally while learning best practices.

Design, build, and productionize machine learning and generative AI solutions. Develop scalable data pipelines, deploy and monitor models, optimize performance, and collaborate with Product, Data Operations, and Engineering to translate business problems into reliable AI-powered applications.

Design, build, and operate end-to-end MLOps infrastructure and pipelines for training, evaluation, deployment, monitoring, and retraining. Own model-serving for batch and real-time workloads, implement CI/CD, containerized orchestration, model/data versioning, observability, and drift detection. Optimize GPU/compute utilization, enable reproducible research-to-production workflows, and evaluate emerging MLOps tools while partnering with researchers and platform teams.

Design, develop, and deploy ML models (classification, forecasting, NLP, LLMs); build scalable data pipelines; integrate AI into production; collaborate with Data Ops, Product, and Engineering; improve model performance and scalability; and implement model monitoring, governance, and lifecycle management.

Lead AI enablement for data, reporting, and analytics within Client Protection. Identify and validate AI use cases, run proofs of concept, pilot solutions, build user guides, present findings, and partner with stakeholders to accelerate AI adoption while ensuring governance and compliance.

Lead end-to-end sales for AWS-based data and analytics solutions in the Indian BFSI market. Identify and pursue net-new opportunities, engage stakeholders, collaborate with AWS partners, and deliver consultative solution selling to meet revenue targets.

Manage end-to-end technology procurement for strategic vendors: own renewals pipeline, lead RFI/RFP and negotiations, draft contracts with legal, align stakeholders, qualify suppliers, resolve post-signature delivery or commercial issues, and provide market intelligence for strategic sourcing decisions.