Top Machine Learning Engineer Jobs in Mumbai

Lead development of machine learning and analytics solutions for global map-making. Design scalable Big Data processes, build and validate predictive models, apply computer vision and point-cloud techniques, work with Hadoop/Spark pipelines, deploy and maintain applications, and lead predictive modeling teams while communicating results to technical and non-technical stakeholders.

Owns product quality frameworks and governance for ESG products across the product lifecycle. Defines quality standards, KPIs, dashboards, controls, and assurance plans; validates features, releases, and methodologies; monitors performance; leads root cause analysis and corrective actions; and drives automation, AI-enabled quality controls, process improvement, and cross-functional transformation with Product, Research, Technology, Data Operations, and Client Services.

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.

Lead design, build, and maintain secure, scalable software systems; write and review production-quality Python code; drive adoption and safe use of enterprise AI-assisted engineering tools; improve automation, resiliency, and operational stability; evaluate vendors and lead engineering communities of practice within Commercial and Investment Banking.

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 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.

Lead scientific and strategic AI efforts for Vaccines Medical: define AI vision and roadmap, govern responsible AI standards, prioritize and deliver validated AI-enabled solutions, partner cross-functionally, oversee deployment and adoption, manage budget and vendors, and ensure regulatory, ethical, and scientific compliance across global medical affairs.

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.

Develop and improve digital products using .NET Core and related frameworks. Implement features, fix issues, collaborate with global product teams, research solutions, and follow development best practices.

Sell SailPoint's IGA identity security SaaS to $5B+ enterprises by engaging C-level stakeholders, managing long sales cycles with POCs/RFPs, collaborating with GSIs and channel partners, developing account and opportunity plans, and meeting quota using Challenger methodology.

Join the Digital Fraud Strategy team to analyze digital payments fraud, develop detection strategies and rules, reduce fraud losses and alert costs, automate reviews, and improve customer experience. Use programming, ML models, and cross-functional collaboration to design and implement fraud-fighting products and present findings to senior leadership.

Lead design and adoption of cloud-native architecture and AI-enabled engineering across Asset & Wealth Management. Advise on technology selection, create scalable reusable frameworks, develop and review secure production code, set multi-year roadmaps, and establish governance and guardrails for agentic AI, automation, and responsible AI use at scale.

Design and scale a unified AI/ML data collection platform supporting data pipelines, LLM and RAG workflows, model training, evaluation, deployment, observability, and lifecycle management. Build production-grade distributed and cloud-native systems, implement MLOps practices, monitor model performance and reliability, optimize costs, and mentor engineers while collaborating with product, research, engineering, and business teams.

Leads development of secure, scalable Java and Spring Boot microservices and APIs. Designs, tests, debugs, and operationalizes production software while improving engineering productivity through AI-assisted development tools. Builds AI agents and LLM-enabled workflows using Python, with appropriate grounding, validation, security, and guardrails. Supports Kubernetes operations, CI/CD, application resiliency, and responsible adoption of AI engineering practices across the team.

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 design, deployment, and management of prompt-based LLM models for NLP in financial services. Build data pipelines, MLOps tooling, and APIs; research prompt engineering and orchestration techniques; evaluate and optimize model performance; collaborate with cross-functional teams and communicate results to stakeholders.

Design, build, and operationalize a unified AI/ML data collection platform. Responsibilities include LLM/RAG pipelines, data ingestion, feature management, model lifecycle and deployment, observability, scalable distributed systems, and mentoring engineers to deliver production-ready ML solutions.

Build and deploy production-grade Generative AI and LLM applications, including RAG pipelines, document processing, natural-language-to-SQL solutions, evaluation frameworks, synthetic-data pipelines, and backend APIs. Integrate vector and relational databases, optimize inference, monitor AI services, and deploy with Docker and cloud environments. Collaborate with product, backend, and domain teams to deliver secure enterprise AI solutions.

Leads consultative presales for credit and fraud risk solutions across Asia Pacific. Partners with clients and sales teams to discover challenges, develop proposals, business cases, platform demonstrations, executive presentations, and deal strategies. Serves as a subject matter expert across the risk lifecycle, payment ecosystems, authorization, authentication, lending, fraud, and collections. Supports go-to-market initiatives, innovation workshops, thought leadership, and proof-of-concept engagements, with travel of up to 50%.

Lead design of credit underwriting strategies and customer journeys across digital and phygital channels; perform market and portfolio analytics to identify growth and risk reduction levers; build acquisition and behavioral scorecards; design experiments for new channels; track credit risk performance and diagnostics; manage 2-3 analysts while leveraging Python, SQL, Excel, visualization and ML techniques.

Design, develop, and maintain production ML solutions for trade surveillance and financial crime. Perform end-to-end model lifecycle work (feature engineering, training, validation, calibration, monitoring), support model governance and audits, collaborate with SMEs and MLOps for CI/CD and productionization, deliver interpretable explainability outputs, and incorporate GenAI/LLMs for unstructured text signal extraction.

Design, develop, and maintain production ML solutions for trade surveillance and financial crime use cases. Apply supervised and unsupervised methods, execute model lifecycle (feature engineering, training, validation, monitoring), support model governance, partner with MLOps for CI/CD, deliver explainable outputs, and leverage GenAI/LLMs for unstructured data extraction and summarization.

Analyzes business requirements, processes, and workflows; designs future-state operating models; identifies automation and AI opportunities; supports RPA and intelligent automation delivery; owns UAT planning and execution; coordinates stakeholders across business, technology, risk, and operations; and supports transformation, testing, governance, change adoption, and continuous improvement initiatives.

Leads technical Developer Relations for NVIDIA AI platforms across India's financial services AI labs. Builds adoption strategies, technical demos, benchmarks, reference architectures, workshops, and performance guides. Works directly with researchers, infrastructure teams, and CTOs to profile workloads, optimize inference, debug integrations, and design secure, observable GPU deployments across cloud, data centers, Kubernetes, and hybrid environments. Translates developer feedback and workload requirements into product and engineering input.