Top Machine Learning Jobs in India

Lead AI/ML development initiatives: design algorithms, mentor engineers, manage development process, and deploy AI solutions to production.

Design, build, deploy, and maintain production AI/ML applications across the full ML lifecycle. Develop and productionize models, LLM-based and generative AI solutions, create scalable APIs and MLOps pipelines, optimize performance and cost, and collaborate with product and data teams. Mentor engineers and promote engineering best practices.

Support marketing analytics by implementing machine learning models, automating reporting and model scoring, validating models with NY counterparts, translating business requirements, and streamlining analytics processes using Python, SQL and cloud platforms.

Build end-to-end machine learning solutions for business stakeholders using advanced ML and statistical methods. Work with big data (Spark/PySpark, SQL), AWS, and collaborate with cross-functional teams to validate models, demonstrate value, and deploy scalable analytics, with a product mindset and agile delivery.

Analyze e-commerce, finance, growth, and operational performance to deliver actionable insights. Build forecasting and predictive models, evaluate projects and KPIs, support stakeholder decision-making, and foster a data-driven culture to improve business performance across departments.

Develop and optimize deep learning, graph, and ML techniques for GPU architectures; work with customers to deliver AI solutions; collaborate with architecture, research, libraries, and system software teams.

Design and build discrete-event, continuous/rate-based, and agent-based AnyLogic models of flexible pipe production to analyze bottlenecks, utilization, WIP, storage, downtime, and throughput. Produce scenarios, dashboards, and reports, integrate models with ML, collaborate with production, engineering, and supply chain teams, maintain models, and support capacity planning and optimization with international collaboration and travel.

Design and implement scalable AI/ML and data-driven cloud or Kubernetes solutions. Build data and model pipelines, integrate enterprise CRMs, operationalize models with dev teams, manage stakeholders, and support CRM-driven and agentic AI use cases across concurrent projects.

Lead cross-site capacity modeling, scheduling, and productivity initiatives for front-end semiconductor manufacturing. Develop analytical and optimization tools, apply operations research and Factory Physics to reduce cycle time and optimize WIP, validate scheduling logic, mentor junior engineers, and partner with operations, supply chain, and IT to support capacity planning and fab expansion.

Build and own backend services for millions of MAUs, design scalable systems, and ship production AI/ML features (voice agents, chat, recommendation and memory engines). Automate key workflows and operate end-to-end ownership of services and stacks.

Lead design, build, and optimize scalable data integrations and platforms using OIC and GCP. Develop ETL/ELT pipelines, manage APIs, ensure data quality, troubleshoot integrations, collaborate with stakeholders, document solutions, and apply best practices for secure, analytics-ready data.

Technical lead responsible for designing and delivering enterprise web and mobile features, owning features end-to-end, leading and mentoring a distributed engineering team, influencing product design, implementing ML/NLP solutions, and ensuring compliance with performance, privacy, and security requirements.

Conduct assessments in sales, marketing, and supply chain; analyze data to identify trends and solutions; collaborate globally to drive revenue and innovate solutions.

Manage client relationships, develop technology solutions, oversee project delivery, and lead a cross-functional team. Focus on business development and talent retention.

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

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.

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.

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

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

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.

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 quality control for annotated construction data (CAD/BIM/MEP). Review and validate annotator outputs, perform AutoCAD annotation, collaborate with CV/ML engineers to improve model-data alignment, manage and mentor annotators, and drive process/tooling improvements with engineering leads.