Top Machine Learning Jobs in India

Provides technical leadership for enterprise-scale, cloud-native applications and AI-driven platforms. Defines architecture vision and roadmaps, designs scalable solutions, leads initiatives from architecture through delivery, and advises customers and stakeholders. Oversees architecture and code reviews, technical escalations, engineering standards, mentorship, product quality, reliability, performance, and AI adoption. Evaluates emerging technologies and provides strategic architectural oversight across products and teams.

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.

Own commercial demand and revenue forecasting for oncology and hematology business areas. Build patient-based and driver-based models, budgets, rolling forecasts, strategic plans, and scenario analyses. Translate market research and industry datasets into forecasting assumptions, develop gross-to-net revenue estimates, analyze forecast variances, and support cross-functional commercial finance decisions with teams across finance, accounting, market access, and commercial operations.

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.

Develop and deploy production-grade AI and machine learning solutions, including LLMs, RAG, transformers, deep learning, recommendation systems, regression, classification, and clustering. Conduct rigorous statistical analyses, apply FATE principles, translate insights into business strategies, and establish ML production best practices. Collaborate with engineering, product, and analytics teams using Python, SQL, Azure, Snowflake, and modern ML frameworks.

Lead QA strategy and mentor QA engineers for AI and machine-learning forecasting products. Oversee automated test frameworks, BDD and Gherkin practices, REST API validation, ML model testing, defect tracking, release criteria, and CI/CD quality improvements. Collaborate with product, data science, and engineering teams to ensure reliable, scalable AI systems.

Lead the transition to an AI-driven, automated SOC by defining the automation roadmap, overseeing SOAR integrations and Detection as Code pipelines, mentoring engineers, and applying machine learning to security data. Serve as incident commander for major breaches, supervise threat hunting and intelligence programs, manage SIEM, EDR, NDR, and cloud security integrations, and optimize workflows to reduce detection and response times.

Leads Level 3 cyber defense monitoring and complex incident escalation while engineering an AI-driven SOC. Designs autonomous agents, SOAR playbooks, detection-as-code workflows, and LLM-integrated investigation pipelines. Applies machine learning and data analytics to improve threat detection and reduce false positives. Conducts threat hunting, oversees SIEM/EDR/NDR platforms, integrates threat intelligence, and optimizes SOC workflows to reduce detection and response times.

Develop and execute verification and validation strategies for embedded software, power-system protection and control products, and industrial communication protocols. Build Python-based automated test frameworks, simulators, and AI-assisted testing agents; perform interoperability, integration, regression, performance, and fault-injection testing. Analyze logs and network traces, troubleshoot firmware and system issues, and conduct root-cause analysis across relays, IEDs, RTUs, SCADA systems, and substations. Maintain CI/CT pipelines and ensure AI-driven testing remains traceable, explainable, repeatable, and human-validated.

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.

Leads the launch and development of Nature Robotics, providing scientific, editorial, strategic, and operational leadership. Responsibilities include assessing manuscripts, making editorial decisions, commissioning and editing content, mentoring the editorial team, setting journal strategy, managing portfolio growth, upholding publishing ethics, engaging research communities, and representing the journal internationally. The role requires deep robotics expertise, strong scientific judgment, editorial experience, leadership, communication skills, and a relevant PhD.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Contribute to a live Yard Management System through computer vision and OCR prototyping or backend feature development. Responsibilities include building and evaluating detection pipelines, benchmarking models, assessing edge deployment, implementing workflow and inventory features, writing tested code, participating in reviews, documenting work, and developing automation and integrations.

Leads development of AI-powered engineering applications and data platforms for semiconductor development. Builds LLM, RAG, vector database, and agent-based solutions; scalable data pipelines; backend APIs; React and TypeScript applications; analytics; workflow automation; and engineering knowledge platforms. Partners with architecture, design, verification, and product engineering teams, while setting technical direction, reviewing architecture, mentoring engineers, and influencing technology strategy.

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.

Work on ML/DL and computer vision models for aerospace and defense (missiles, rocket telemetry, avionics). Train, test, and improve models; prepare and clean text/image/sensor datasets; build lightweight demo APIs with FastAPI/Flask; document experiments and collaborate with mentors on real-world avionics systems.

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 the architecture, development, and deployment of enterprise AI systems using LLMs, RAG, multimodal models, and multi-agent architectures. Drive prototyping through production delivery, establish LLMOps practices, and ensure model reliability, security, and governance. Serve as a technical advisor during presales, collaborate with product and data teams, and mentor junior engineers.

Develop and own end-to-end machine learning and data engineering solutions, from exploring structured and unstructured datasets through production deployment and monitoring. Apply advanced machine learning, NLP, deep learning, and statistical techniques to business problems; improve existing models; coordinate with engineering and client teams; define requirements, timelines, and success metrics; and resolve production issues.

Designs, develops, and maintains scalable software solutions integrating AI and machine learning models into commercial products and internal processes. Collaborates with data scientists, product managers, and cross-functional stakeholders; writes high-quality code; reviews architectures; supports product launches and process improvements; and follows Agile, NPI, and deployment best practices.

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.

Leads GitLab’s India and Asia-Pacific Engineering and IT recruiting organization, including recruiting managers and individual contributors. Owns regional talent strategy, senior and executive technical searches, workforce planning, hiring metrics, sourcing strategies, and recruiting operations. Advises senior engineering and IT leaders on talent markets, organization design, compensation, and hiring priorities while strengthening candidate experience, diversity, leadership capability, and scalable remote collaboration.

Leads architecture and development of a cloud-based, multi-tenant network management platform. Responsibilities include building AI/ML network analytics, enterprise integrations, high-throughput telemetry processing, zero-trust security, APIs, edge gateways, scalable distributed systems, and hybrid cloud solutions. The role mentors engineering teams, establishes software development practices, optimizes performance, and works with enterprise customers on complex integrations. Requires extensive software development and network management experience, strong networking expertise, and knowledge of major network vendors.