Top Machine Learning Jobs in India

Build, test, and deploy scalable backend components (primarily Django/Python). Implement algorithms and subsystems for lending and user communications. Solve complex engineering problems with simple designs. Collaborate cross-functionally and perform analytics using SQL to inform product and marketing decisions.

The AI Lead will design, develop, and implement AI solutions to enhance the SDLC stages, mentor teams, and ensure responsible AI practices.

Pravāh seeks candidates able to propose their own roles to contribute to AI solutions for electric grid management and operations, focusing on high agency, ownership, and strategic thinking.

The AI Solution Architect will design and implement AI solutions, collaborate with engineering teams, evaluate client needs, and drive architectural decisions for scalable AI systems.

As a Senior Impact Analyst, you will analyze sustainability challenges, design and manage interventions, and work closely with smallholder communities, emphasizing rigorous research and collaboration.

The role offers veterans a one-week unpaid placement to learn about AI and data science in a leading UK tech company.

As a Staff Research Scientist, you will lead research on perception and machine learning for humanoid robots, collaborating with teams and presenting findings.

Lead end-to-end AI solutions for clients, manage project delivery and people, partner with sales for business development, collaborate with engineering/MLOps/DevOps, ensure data quality, model training, tuning, deployment, monitoring, and provide executive updates on status and risks.

Seeking a Senior Engineer with Machine Learning expertise to innovate solutions, guide teams, and tackle complex client challenges, enhancing overall outcomes.

The Staff Engineer in Machine Learning will guide teams, solve client challenges, and enhance solutions leveraging their technical expertise.

Lead technology vision and architecture for enterprise projects; design cloud-native, data-driven and Generative AI solutions; ensure scalability, security, governance, and engineering excellence; run architecture reviews, POCs, and implementations; evaluate emerging technologies; influence clients via workshops and executive engagements; mentor architects and distributed engineering teams.

As a Staff Engineer in Machine Learning, you will lead technical direction, drive product development, and support team growth through advanced solutions.

Lead RTL front‑end EDA methodologies and low‑power initiatives. Provide expert support for RTL power analysis and simulation, develop automation frameworks (Python/Tcl/Shell), drive generative‑AI adoption in EDA workflows, interface with EDA vendors, document methodologies, and mentor junior engineers while working on‑site in Pune.

Lead implementation of AI and automation across legal functions: deploy contract intelligence, fraud detection, and KYC automation; digitize manual legal workflows and inbox triage; build self-service document tools; maintain the legal tech stack and KPIs; train legal teams to drive adoption. First-six-month goals include KYC digitization, AI-driven inbox triage, and a contract analysis tool that reduces review time by 30%+.

Lead end-to-end advanced analytics and ML solutions: analyze large datasets, build ETL pipelines, develop and deploy ML/GenAI models, optimize performance, ensure data quality and governance, and collaborate with product, engineering, and business teams.

Lead development and implementation of complex control algorithm models (Matlab/Simulink) for HVAC, electrification, and power electronics. Perform transient analysis, MIL/HIL simulation, auto-code generation, partner across teams, evaluate new technologies, drive system-level optimization, mentor team members, and contribute to technology roadmaps and externally funded projects.

Design, build, test, deploy, and maintain large-scale distributed systems handling hundreds of millions of requests daily. Lead architecture, code reviews, releases, on-call incident response, cross-functional delivery, mentorship, and initiatives to improve system quality and KPIs.

Build and evaluate measurement frameworks and ML models to analyze customer journeys and conversational/support data. Design experiments and surveys, partner with Data Engineering to create scalable metrics/views, and produce Power BI dashboards to communicate insights. Apply advanced statistical methods, clustering, survival and Markov models, generative AI/RAG techniques, and mentor junior data scientists.

Lead end-to-end data science projects using machine learning and statistical modeling to deliver actionable insights, mentor junior scientists, align solutions with business objectives, and contribute to the team's strategic vision and methodology improvements.

Lead end-to-end data science projects by developing advanced machine learning and statistical models, delivering actionable insights, mentoring junior data scientists, and contributing to the Data Science teams strategic vision and methodological improvements.

Lead design and development of scalable AI algorithms and systems, collaborate with product and cross-functional teams to integrate solutions, manage end-to-end AI project lifecycle, and mentor AI engineers to drive technical excellence and alignment with business goals.

Design, build, and maintain data pipelines, data warehouses and lakes; implement data processing and analysis algorithms; collaborate with data scientists to deploy ML models; lead performance engineering, system stability and scalability efforts; partner with cross-functional teams and ensure compliance with controls and service-level expectations.

Design, build, and maintain end-to-end analytics and BI solutions: data models, transformations, and interactive dashboards. Use SQL, Python/PySpark, notebooks, and BI tools (Power BI, Gen-UI) to deliver insights, predictive models, generative-AI use cases, and stakeholder-facing reporting. Document data flows, ensure data quality, and partner with business leaders to drive performance insights and decision-making.

12-month apprenticeship supporting AI product development: gather requirements, conduct user and market research, document user stories, assist backlog prioritization, track adoption metrics, collect feedback, support demos/training, and coordinate stakeholder communications to drive AI product adoption.

Lead design, implementation, testing, deployment and maintenance of high-quality distributed systems for TMS/WMS. Own technical direction and roadmap, hire and mentor engineers, deliver features in an Agile/DevOps environment, and apply ML and geolocation integrations to improve service performance, reliability, cost, and security.