Top Machine Learning Engineer Jobs in Mumbai

Supports Citi’s internal KYC program by creating, validating, updating, and completing KYC records using internal and external research. The role ensures regulatory and policy compliance, monitors workflow progress, meets service-level targets, escalates discrepancies and high-risk or negative-news cases, and reports operational metrics. It also involves collaboration with relationship management, compliance, and case-management teams, along with participation in departmental projects.

Deliver analytical consulting services, develop credit risk models using bureau and alternative data, advise on acquisition and credit-limit strategies, and translate technical findings into commercial value. The role requires advanced statistical and machine learning expertise, large-dataset analysis using Python and SAS, MSME lending knowledge, portfolio risk management experience, and the ability to manage complex projects and collaborate with business and technical teams.

Conduct applied AI and machine learning research for Microsoft products. Design, train, fine-tune, and evaluate models including LLMs and SLMs; analyze large datasets; build scalable prototypes; design experiments and benchmarks; optimize model performance; and collaborate with researchers, engineers, and product teams to deploy research innovations. Document findings through technical reports, publications, patents, or open-source work.

Manages science and technology initiatives for sustainable industrial fisheries. Leads multidisciplinary projects involving electronic monitoring, AI, machine learning, computer vision, data systems, hardware, software deployment, and seafood traceability. Oversees vendors, contractors, budgets, procurements, technical assessments, and deployment of systems in remote environments. Translates technical concepts for diverse stakeholders and supports technology evaluation, scaling, and conservation strategy. The role may require domestic and international travel of 25% or more.

Develop artist-facing and backend tools that extend SideFX Houdini and integrate with VFX and CG production pipelines. Build clean, testable, maintainable software using HOM, HDK, Python, and/or C++. Collaborate with artists, developers, and project managers across the software lifecycle, while emphasizing usability, documentation, UX, reliability, and adoption. Support procedural workflows, production tools, simulations, rendering, and distributed studio systems.

Build Python-based tools for layout, animation, matchmove, and previs artists working with complex CG scenes. Develop scene-management systems, asset and animation library integrations, and 3DEqualizer data import/export workflows. Collaborate with product owners and stakeholders in an Agile environment, contributing across design, development, maintenance, and pipeline integration. Work may include front-end and back-end development for high-end VFX and feature animation pipelines.

Develop artist-facing and backend tools that extend Autodesk Maya and integrate with VFX and CG pipelines. Build reliable, maintainable software using HOM, HDK, Python, C++, MEL, and Maya APIs; improve workflows, usability, and adoption; and collaborate with artists, developers, and project managers throughout the software lifecycle. The role also involves documentation, testing, deployment, support, roadmap planning, and innovation across distributed VFX production teams.

Leads strategy, roadmap, and end-to-end development of ML-powered generative AI products. Defines requirements, prioritizes backlogs, integrates ML models into scalable experiences, tracks product metrics, evaluates emerging technologies, and coordinates cross-functional teams spanning ML research, engineering, data, UX, and creative functions.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

Lead enterprise data science solutions spanning machine learning, deep learning, Generative AI, Agentic AI, NLP, and RAG. Frame business problems, develop and evaluate models, design production AI workflows, and collaborate on deployment, monitoring, MLOps, governance, and Responsible AI. Provide technical leadership, mentor junior team members, manage stakeholder relationships, and establish data science best practices across the organization.

Develops enterprise data science and machine learning solutions across forecasting, classification, optimization, ecommerce, and market mix modeling. Responsibilities include sourcing and engineering data, building scalable models, communicating insights, collaborating with technical teams, creating automation solutions, and establishing data science standards and processes. The role also supports innovation involving generative and agentic solutions.

Develop scalable AI and machine learning solutions for forecasting, optimization, classification, ecommerce, and operational analytics. Partner with stakeholders and engineering teams to deploy, monitor, and improve models. Apply supervised and unsupervised learning, NLP, time series, and mathematical optimization techniques. Contribute to data science standards, documentation, knowledge sharing, and community practices while translating business needs into measurable outcomes.

Leads AI and data science strategy, model development, technical exploration, deployment, and post-production performance management. Manages and mentors a team of data scientists, prioritizes projects and resources, partners with stakeholders, and promotes AI literacy. The role requires expertise in machine learning, MLOps, Vertex AI, generative and agentic AI, stakeholder engagement, and people leadership.

Designs, develops, and operates enterprise automation and AI-enabled solutions using ServiceNow, Power Platform, machine learning, and agentic AI. Builds workflows, portals, applications, integrations, and CSM solutions; supports platform modernization, governance, security, testing, and production stability. Provides technical leadership, troubleshooting, vendor coordination, stakeholder collaboration, and team coaching while driving adoption of emerging automation technologies.

Develop advanced analytics, AI/ML models, data pipelines, dashboards, and reporting solutions for Identity and Access Management controls. Analyze identity and entitlement data to detect anomalies, toxic access, control gaps, and IAM risks. The role also embeds model governance, AI risk management, explainability, monitoring, audit traceability, and responsible AI practices while partnering with security, risk, audit, compliance, privacy, and technology stakeholders.

Build and validate production machine learning and statistical models for music and entertainment marketing. Develop real-time data systems, predictive scoring, trend detection, AI briefing classification, dashboards, and GenAI-assisted workflows. Collaborate with data engineering, paid media, and creative teams while implementing tokenization, anonymization, and cross-border data privacy controls. The role is a 12-month fixed-term contract supporting clients across APAC.

Lead the architecture, development, deployment, and monitoring of production-grade machine learning systems for high-definition, ADAS, ISA, and IVI maps. Lead work across computer vision, NLP, deep learning, Generative AI, Agentic AI, MLOps, data ingestion, model training, inference, and scalable cloud or in-vehicle deployment. Provide technical leadership, conduct architecture and code reviews, integrate ML outputs with mapping workflows, and translate business requirements into reliable ML system designs.

Leads targeted, end-to-end recruitment for niche Data, AI, and Data Science roles. Develops sourcing strategies, conducts proactive headhunting, maps talent markets, engages passive candidates, and builds technical talent pipelines. Partners with executive and engineering stakeholders, manages candidate relationships and offer negotiations, and provides market intelligence on skills, compensation, competitors, and talent availability. Requires sufficient technical familiarity to distinguish specialized data and AI skill sets during screening.

Develop, train, evaluate, and optimize ML solutions and data pipelines; translate business requirements into production-ready ML code; perform feature engineering, model selection, and evaluation; document work, participate in code reviews, and support testing and data issue resolution.

Own product vision, strategy, roadmap, requirements, feature prioritization, development, launches, and post-launch performance analysis. Partner with customers, engineering, marketing, and cross-functional stakeholders to deliver user-focused SaaS solutions, prioritize business value, and drive measurable outcomes.

Designs and maintains ETL pipelines integrating banking-system data into analytics platforms; builds data models for reporting, analytics, and machine learning. Collaborates with analysts, data scientists, and stakeholders to define requirements and delivers insights across risk, credit, and liabilities. Ensures data quality, security, governance, and compliance while monitoring and troubleshooting workflows and infrastructure performance.

Build production-grade AI and machine learning security products using Go services and Python pipelines. Develop LLM-powered analysis, RAG, classification, structured extraction, and agentic AI capabilities; establish evaluation datasets, metrics, regression suites, and drift monitoring. Collaborate with security researchers to translate threats into actionable detections, and deploy cloud-native solutions using Docker, Kubernetes, and major cloud platforms.

Leads biomedical data science for clinical and health research, integrating clinical, observational, EHR, genetic, wearable, electrophysiological, and video data. Develops signal-processing methods, machine-learning models, probabilistic and time-series analyses, scalable data pipelines, and reproducible workflows. Validates clinically relevant models, supports disease phenotyping and predictive modeling, ensures compliance with pharmaceutical and healthcare standards, and communicates technical findings to clients and stakeholders.

Design and develop scalable full-stack features using Java, Spring Boot, Python, FastAPI, Angular, and Azure. Lead technical decisions, backend architecture, API design, code reviews, engineering standards, deployment strategies, CI/CD improvements, cloud deployments, and observability. Collaborate with product, data science, and design teams to build AI-powered user experiences, contribute technical insights to roadmap planning, and balance hands-on coding with technical leadership.

Build, deploy, and maintain production-grade machine learning systems on Google Cloud Platform. Develop generative AI applications, RAG systems, agentic and multi-agent workflows, traditional ML, and computer vision solutions. Own MLOps practices including CI/CD, testing, model versioning, deployment, monitoring, and drift detection. Build APIs, microservices, knowledge graphs, and semantic search systems while collaborating with architects, data engineers, analysts, and enterprise clients.