Top Machine Learning Engineer Jobs in Mumbai

Designs and oversees enterprise software architecture, development, testing, implementation, and technical documentation. Partners with stakeholders and Product teams to define requirements, evaluate technologies, create scalable solutions, review code, support application integrations, and guide development teams. The role also coordinates technology projects, training, system improvements, security, quality, and extensibility while providing technical support for case and client efforts.

Supports business intelligence projects involving SQL, Python, data analysis, ETL, data migration, reporting, dashboards, forecasting, and basic machine learning. The intern collaborates with BI, data engineering, product, and other teams to prepare data, generate insights, document work, and support business requirements while gaining hands-on experience with real business data.

Develop, validate, debug, and optimize machine learning models, training pipelines, inference systems, and numerical implementations using Python. Build reproducible workflows, diagnose computational issues, create systematic tests, and improve memory usage, throughput, and performance. Review code and solutions, use coding agents in development, document technical findings, and deliver reliable work independently within project deadlines.

Supports Citi’s KYC and AML compliance program by creating, validating, updating, and completing KYC records. Reviews corporate and regulatory information, verifies ownership and management details, tracks workflow progress, meets service-level targets, escalates discrepancies and high-risk cases, maintains operational records, and reports metrics and issues to management. The role also assists with departmental projects and applies regulatory and internal policy requirements.

Design, build, and scale production AI agent workflows using AWS Bedrock AgentCore, Step Functions, Claude, MCP, RAG, and guardrails. Develop Python/FastAPI backends, agent-facing interfaces, document-processing pipelines, enterprise integrations, and AWS infrastructure. Implement observability, evaluations, security controls, human approval workflows, and CI/CD. Work directly with enterprise customers and nontechnical stakeholders to deliver end-to-end AI solutions with minimal guidance.

Lead design and implementation of ML tools for scenario classification, anomaly detection, sensor data quality, behavior analytics, and automated reporting. Build scalable pipelines and automated data curation for camera, radar, and LiDAR; create SIL/HIL replay interfaces, ground-truthing and annotation tools, and offline benchmarking. Integrate ML modules into simulation, HIL/SIL, and CI/CD ecosystems, implement reproducible workflows (Docker, CI/CD, artifact versioning, automated tests), monitor model/data quality and mentor junior engineers while collaborating with verification and perception teams.

Design and deliver production-grade AI/ML architectures for education and assessment platforms. Translate stakeholder requirements into secure, cost-aware blueprints covering RAG, multi-agent systems, LLM judging, OCR, human review, cloud infrastructure, LLMOps/MLOps, observability, governance, and evaluation. Lead workshops, architecture decisions, production readiness, FinOps, sovereign hosting, and technical mentorship across international government and education programs.

Design, develop, and deploy ML models on Google Cloud (Vertex AI). Process and feature-engineer large datasets in BigQuery, integrate ML pipelines with GCP services, collaborate with stakeholders, and produce analytical reports and visualizations to support data-driven decisions.

Develop and fine-tune machine learning, NLP, and deep learning models using Python and major ML frameworks. Work with transformer architectures and proprietary enterprise data, process documents using OCR and PDF parsing tools, and conduct rigorous model evaluation, statistical sampling, and error analysis.

Leads advanced analytics, KPI governance, process mapping, and digital transformation across global procurement, supply chain, fulfillment, logistics, manufacturing, and commercial operations. Develops dashboards, predictive analyses, SAP-based reporting, and actionable insights to improve inventory, fulfillment, supplier performance, forecasting, and customer service. Partners with global stakeholders on process standardization, technology adoption, change management, and continuous improvement initiatives.

Own Tala’s product strategy and roadmap for fraud prevention, authentication, identity, and risk capabilities. Partner with Engineering, Data Science, Analytics, and business teams to build data-driven fraud products, define decisioning metrics and thresholds, manage fraud incidents, improve customer security, and minimize friction. Lead cross-functional execution from discovery through launch and iteration while managing external technology partners.

Implement machine learning models in production and design enterprise GenAI, LLM, and AI automation solutions. Build and manage industrialized processing pipelines, establish ML lifecycle and MLOps/LLMOps best practices, integrate Microsoft AI and Power Platform services, and support data science teams. The role also involves gathering requirements, estimating work, presenting technical solutions, troubleshooting AI agents, and creating documentation.

Implement machine learning models in production and design enterprise GenAI solutions, AI agents, and scalable LLM/ML automation. Build and manage industrialized processing pipelines, MLOps and LLMOps frameworks, and model lifecycle best practices. Integrate Microsoft Copilot Studio and Azure AI services with Power Platform tools, implement RAG and knowledge grounding, support multilingual retrieval, and troubleshoot agents. Gather requirements, estimate work, present technical solutions, and create documentation.

Leads consultative discovery and solution development for AspenTech customers across Oil & Gas, Chemicals, Power, Mining, and Food & Beverage industries. Partners with sales teams to identify business challenges, demonstrate software capabilities, develop value propositions, create proposals and scopes of work, and support opportunities through solution design and delivery. Requires strong industry, technical, analytical, communication, and consultative expertise, with substantial regional travel across APJ.

Build scalable, low-latency backend APIs, event-driven services, integration layers, and microservices supporting personalization and recommendation experiences. Partner with AI/ML and data engineers to deploy models, expose AI services, and develop React applications. Improve security, observability, reliability, performance, scalability, and operational efficiency while using AI-assisted engineering tools to enhance delivery and code quality.

Leads the design, development, and deployment of advanced data science and AI solutions across business areas. Provides hands-on technical leadership, solution architecture, stakeholder engagement, mentoring, and portfolio-level decision-making. Applies machine learning, GenAI, LLMs, RAG, AI agents, graph technologies, and cloud platforms to deliver measurable business impact and improve operational efficiency and decision-making.

Analyzes US healthcare claims and related datasets to identify data-quality issues, patterns, coverage gaps, and product opportunities. Develops SQL queries, dashboards, validation processes, documentation, and test scenarios; investigates defects and performs root-cause and impact analysis. Collaborates with product, engineering, machine learning, engagement, and account teams to support healthcare data-product delivery, client data services, product enhancements, and proof-of-concept initiatives.

Leads emerging product innovation from opportunity discovery through prototyping, pilots, product-market validation, commercialization, and scale. Owns AI, data, analytics, automation, and platform product strategies; establishes experimentation and stage-gate governance; develops investment cases and go-to-market plans; and aligns cross-functional teams and executives. Manages product leaders, builds incubation practices, and drives measurable customer, revenue, efficiency, and strategic outcomes.

Lead targeted, end-to-end recruitment for niche Data, AI, Machine Learning, and Data Science roles. Responsibilities include technical headhunting, passive candidate engagement, talent mapping, digital sourcing, market intelligence, stakeholder partnership with technology executives, candidate management, and offer negotiation. The role requires strong technical recruiting expertise, familiarity with modern data and cloud technologies, advanced search capabilities, and the ability to distinguish specialized technical skill sets.

Design, build, deploy, and operate production AI/ML systems on Google Cloud. Develop clean, tested Python; evaluate model quality; manage cloud networking and security; and ensure data and models remain within client boundaries. Explain AI trade-offs to clients, collaborate with Google Cloud teams, and own outcomes from prototype through reliable production use.

Build reinforcement learning environments for coding tasks, develop and deploy production AI agents, and shape technical direction. The role also involves leading engineering growth in a fast-paced startup. Candidates need 3–7 years of engineering experience, strong reinforcement learning fundamentals, experience with coding-focused reinforcement learning, simulated training environments, autonomous agents in production, and AI coding assistants. The position is remote in India with a 5.5-day workweek and Pacific-time overlap.

Lead end-to-end ML systems: data pipelines, model development (transformer-based detection), sim-to-real synthetic data, multi-sensor fusion, on-device real-time inference (Jetson), MLOps (experiment tracking, versioning), and production deployment and monitoring. Set ML roadmap and hire/lead as the team grows.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Conduct machine learning and computer vision research focused on image restoration, enhancement, generative models, 3D vision, detection, and tracking. Develop, optimize, and evaluate accurate, lightweight neural network models; formulate solutions and metrics; publish research; and transfer practical innovations into Fynd products. The role requires strong mathematical foundations, image-processing expertise, programming skills, and experience with deep learning frameworks.