Top Machine Learning Engineer Jobs in Mumbai

Design, train, fine-tune, deploy, and optimize production AI systems including LLMs, RAG pipelines, agentic workflows, and machine learning solutions. Build APIs and inference pipelines, evaluate quality and performance, monitor production systems, and translate research into scalable applications. Collaborate with engineering, product, leadership, and global research teams while working a required overnight India schedule aligned with U.S. hours.

Develop statistical and machine learning models to optimize Akamai’s global mapping service. Analyze massive telemetry streams, build predictive observability tools and automated data pipelines, define reliability metrics, investigate outages, and guide architecture and technology investment decisions. Collaborate with product engineers and communicate complex quantitative insights to technical and executive stakeholders.

Owns B2B technology marketing execution across brand building, content, campaigns, demand generation, events, thought leadership, customer marketing, and digital engagement. Partners with leadership, sales, delivery, technology, alliances, and customers to translate technical capabilities into compelling business narratives, launch initiatives, and measure impact. The role requires strong project ownership, cross-functional collaboration, communication, and execution in a fast-moving data, analytics, and AI services environment.

Reviews financial services regulations and exchange rulebooks, assesses regulatory changes, and maintains auditable review records. Validates machine-learning topic classifications, provides labelled feedback, and helps maintain regulatory taxonomies. Compares platform rulebooks with authoritative sources to identify structural errors, missing provisions, duplication, and versioning issues. Performs quality assurance against development requirements and collaborates with product, engineering, and software teams to process new regulations and improve the service.

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.

Build data infrastructure and reliable pipelines, analyze product usage, train traditional and LLM-based models, define product metrics, run experiments, and translate findings into product and go-to-market decisions. The role combines data science, data engineering, product analytics, machine learning, and stakeholder communication, with ownership across data modeling, evaluation, experimentation, and product insight generation.

Build backend and platform components for an AI agent layer, including guardrails, policy enforcement, semantic caching, MCP-based tool governance, access controls, and application integrations. Develop scalable APIs and services, integrate across four applications, write comprehensive tests, troubleshoot production and performance issues, and collaborate with architecture and application teams on secure, reliable integrations.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Develop, train, evaluate, and deploy small task-specific ML models for real-time English conversational skills (ASR, pronunciation, grammar, recommendations). Create datasets and task-specific evaluations, improve internal SOTA, mentor engineers, and contribute to hiring.

Designs and delivers production AI/ML systems for enterprise clients, including generative AI, agentic AI, classical ML, data pipelines, and MLOps on cloud platforms. Leads engineers, owns architecture and production operations, runs client discovery, presents to executives, manages scope and risk, and ensures successful handoff. The role is hands-on, client-facing, and requires collaboration with U.S.-based teams on a shifted India schedule.

Leads an integrated data, AI, analytics, and product engineering organization. Owns strategy, roadmaps, architecture, technical standards, production service levels, AI governance, client relationships, budgets, hiring, and organizational growth. Directs delivery of data platforms, dashboards, machine learning models, generative AI solutions, and full-stack applications while ensuring measurable business value, operational stability, responsible AI practices, and strong stakeholder governance.

Architects and deploys production-grade agentic AI and multi-agent systems using LLMs, RAG, vector databases, prompt engineering, and NLP. Designs autonomous workflows, tool calling, memory, routing, evaluation, observability, and guardrails. Builds backend APIs and microservices while implementing testing for model drift, prompt regression, and agent reliability. Requires advanced Python, hands-on production Generative AI experience, and expertise with agent orchestration frameworks and vector search.

Evaluate, benchmark, deploy, and optimize AI models for on-device execution on Android hardware. Responsibilities include integrating models into applications and system services, reducing latency, memory use, and power consumption, applying quantization and compression, leveraging CPU, GPU, and NPU acceleration, and building hybrid local/cloud inference routing with agent systems.

Seeking senior software engineers with proven expertise in at least one technical area, including AI/ML, LLMs, data, full-stack or backend development, automation, cloud infrastructure, and DevOps. Responsibilities include solving complex problems independently, collaborating across technologies, and delivering production-quality projects in a startup environment. Engagements may be project-based or ongoing.

Design, build, and operate production machine learning systems for Bloomreach’s search platform. Responsibilities include query-time ML, LLM and catalog data pipelines, model serving, online features, evaluation, A/B testing, observability, and low-latency optimization across merchants, languages, and domains. The role partners with Search Quality engineers to deploy ranking and relevance models reliably and cost-effectively at scale.

Lead the development, training, deployment, and maintenance of machine learning and generative AI systems. Build predictive, NLP, chatbot, LLM, and agentic AI solutions using cloud platforms, big data technologies, databases, and ML frameworks. Design ETL pipelines, optimize data processing, and create analytics visualizations. The role also involves model fine-tuning, scalable AI application development, and collaborative problem-solving.

Analyze product, customer, and business data to identify trends, risks, and opportunities. Build dashboards, metrics, reports, analytics solutions, and predictive models while supporting data pipelines and AI-powered automation. Partner with Product Managers, Engineers, customers, and business stakeholders to define requirements, improve workflows, validate product ideas, and measure outcomes. Own initiatives from problem identification through delivery while developing expertise in product management, data engineering, AI, and enterprise financial technology.

Supports settlement risk management across payment clearing and settlement activities. Responsibilities include monitoring credit, liquidity, operational, concentration, and participant default risks; assessing exposures, limits, collateral, and breaches; developing risk policies, dashboards, reports, and governance materials; supporting stress testing and control assessments; tracking remediation actions; and providing independent oversight and challenge to risk owners. The role also analyzes risk data, supports GRC tool implementation, monitors regulatory developments, and contributes to AI-enabled risk monitoring.

Build and own scalable AI and Generative AI platforms using Python, microservices, cloud infrastructure, and machine learning models. Design backend architecture, deploy and optimize ML systems, manage Kubernetes and Terraform-based infrastructure, maintain databases and observability platforms, and lead production reliability practices. The role also involves technical leadership, mentoring, architecture decisions, incident response, and translating client requirements into robust technical solutions.

Own enterprise sales and market development for Anthropic’s AI solutions across India’s banking, financial services, and insurance sector. Build executive relationships, navigate regulated procurement and compliance requirements, close complex multi-year deals, develop BFSI go-to-market strategies, and expand lighthouse accounts. Partner with system integrators, legal, compliance, product, and global teams to support scalable deployments and localized AI capabilities.

The Customer Success Business Partner at Lyric drives customer adoption and retention for strategic accounts, leveraging expertise in AI and supply chain operations.

Leads software product development projects and advanced analytics, machine learning, and technology solution delivery. Responsibilities include gathering requirements, defining problems, creating user stories, managing project teams and client expectations, preparing technical documentation, applying Agile and Scrum methodologies, identifying risks, consulting clients, and developing creative solutions and presentations.

Build and productionize machine learning, LLM, and AI solutions with Data Science teams. Design industrialized data-processing pipelines, implement MLOps and LLMOps frameworks, manage model lifecycles, and develop production Python, ML, and cloud solutions using Azure, GCP, Databricks, Spark, and PySpark. Gather requirements, estimate work, document solutions, present results, and support technical best practices.

Serve as a technical expert throughout enterprise sales cycles, partnering with account executives and customers to identify requirements, demonstrate SingleStore solutions, develop and deploy proofs of concept, lead technical evaluations, tune queries, conduct load testing, and support account expansion and channel partnerships. The role requires expertise in distributed databases, data platforms, AI and ML, cloud and containerized environments, programming, and enterprise technology sales.

Lead the design, implementation, deployment, and operation of machine-learning platform capabilities. Build scalable systems for model serving, feature computation, data validation, workflow orchestration, monitoring, and production support. Own complex initiatives and investigations end to end, improve reliability and developer experience, mentor engineers, conduct design reviews, and guide technical strategy while partnering with Data Science, Product, Risk, Fraud, and Engineering teams.