Top Machine Learning Jobs in India

Implement enterprise ML and GenAI solutions in production, including AI agents, LLM automation, RAG, knowledge grounding, and industrialized processing pipelines. Establish ML lifecycle, MLOps, and LLMOps best practices; integrate Microsoft AI and Power Platform services; support deployments across environments; gather requirements; estimate work; present solutions; and document technical implementations.

Lead design and implementation of scalable knowledge graph solutions for pharmaceutical R&D. Build graph data models, integrate heterogeneous scientific datasets, drive adoption of RDF/property-graph technologies, partner with platform and AI/ML teams, ensure performance and governance, define roadmap, and mentor engineers in ontology and graph engineering best practices.

Designs, develops, and validates control algorithms for temperature, pressure, flow, and vacuum systems used in semiconductor equipment. Responsibilities include process modeling, system identification, PID and advanced controller tuning, instrumentation integration, performance analysis, root-cause investigations, laboratory validation, diagnostics, predictive maintenance, and technical documentation. Collaborates with multidisciplinary engineering teams to deliver reliable, optimized automation and process-control solutions.

Lead and grow a team of applied researchers and engineers to build and operate production AI/ML systems (NLP, LLMs, recommender systems) at scale, set technical direction, partner cross-functionally, and drive measurable business and customer impact.

Design and implement scalable backend services, lead full software lifecycle, write design docs, mentor engineers, collaborate cross-functionally, improve performance and quality, and integrate AI tools to boost development productivity.

Designs, experiments with, deploys, monitors, and evaluates machine learning models. Requires full-stack development involving Python, React, AWS, APIs, and SQL, along with statistical and probabilistic foundations. The role supports AI/ML initiatives in a consulting environment serving financial services and energy clients.

Lead engineering strategy, teams, architecture, delivery, and platform outcomes for the Unify Energy SaaS platform. Oversee software, data, integrations, automation, security, reliability, and applied AI capabilities supporting oil and gas information workflows. Partner with product, customers, implementation, and business leaders to prioritize investments, improve delivery predictability, strengthen platform quality, and achieve measurable customer, operational, and financial results.

Lead enterprise-scale data engineering initiatives, building and optimizing batch, real-time, BI, and GenAI-ready pipelines on AWS. Develop data models, feature stores, and distributed processing solutions; support RAG, ML, and LLM workloads. Guide platform architecture, data governance, quality, risk mitigation, and roadmap planning while providing technical oversight across projects. Collaborate with analysts, leadership, Scrum Masters, and globally distributed teams using Agile, DevOps, DataOps, and MLOps practices.

Designs and operates scalable data-management services, APIs, connectors, event-driven integrations, metadata-as-code, data contracts, validation workflows, and CI/CD deployment patterns. The role automates governance, lineage, data quality, certification, schema-drift detection, and enterprise identifier mapping. It also applies AI and LLMs to metadata and data-quality workflows with human oversight. The position partners across engineering, product, architecture, governance, risk, and operations, supports production incidents, and leads teams delivering reusable capabilities across payments and asset-servicing domains.

Analyzes large datasets to solve business problems, build data science and machine learning models, automate dashboards, and generate actionable insights. The role involves SQL and NoSQL data work, experimentation, statistical analysis, visualization, root-cause analysis, and resolving data issues with engineering teams. The analyst collaborates with Product, Marketing, Operations, and Customer Experience stakeholders and presents findings to support executive strategy.

Develop NLP, computer vision, speech, deep learning, and machine learning solutions. Collaborate with AI researchers, lead experiments, mentor team members, conduct applied research, and deploy machine learning models. The role also involves developing and fine-tuning models, potentially including LLMs, and building AI capabilities for education platforms serving children across India.

Lead AI/ML research initiatives focused on developing advanced algorithms, diagnostics, prognostics, and modeling solutions for large-scale energy equipment. Responsibilities include architecting scalable models, analyzing complex datasets, designing experiments, building prototypes, publishing research, developing intellectual property, collaborating with cross-functional teams, and mentoring junior researchers. The role requires applying machine learning to industrial challenges while advancing research in control systems and signal processing.

Leads complex life sciences analytics projects from problem framing through recommendations, ensuring analytical rigor, quality, timeliness, and business impact. Works with real-world and commercial datasets, using Excel, SQL, R, Python, statistical modeling, AI, and machine learning. Responsibilities include client engagement, team leadership, mentoring, SOP development, capability building, proposal support, and deployment of AI-enabled analytics solutions.

Develops ESG and climate data models, pipelines, scripts, dashboards, and analytical systems. Responsibilities include aggregating and structuring diverse datasets, exploratory analysis, statistical modeling, machine learning, data visualization, model evaluation, and building science-based tools that improve platform performance and provide client insights.

Lead development of ESG and climate data models, pipelines, scripts, and dashboards. Apply statistics, machine learning, scientific programming, and data engineering to aggregate, structure, analyze, visualize, and validate large, diverse datasets. Improve the fidelity and performance of climate models and build science-based tools that provide enterprise clients with actionable sustainability insights.

Lead the product strategy and roadmap for merchant lending risk, including rule engines, decision engines, model orchestration, audit-ready decision logs, explainability, policy versioning, and experimentation infrastructure. Partner with Risk, Data Science, Engineering, and Business teams to balance lending growth, credit quality, fraud prevention, monitoring, and regulatory compliance. Manage credit risk analysts responsible for underwriting and risk operations.

Design, develop, evaluate, deploy, and maintain reliable agentic AI systems. Responsibilities include LLM workflow engineering, prompt and tool design, SQL-based investigation, output verification, evaluation framework development, root-cause debugging, safeguards, validation, retries, and translating ambiguous business issues into reproducible technical problems.

Lead development of ML-powered search relevance systems at scale, including ranking, retrieval, query understanding, query rewriting, NLP pipelines, and search evaluation. Build automated ML pipelines and offline/online evaluation frameworks, drive experimentation, and collaborate with product and cross-functional teams to advance Databricks’ search and discovery experience.

Design, implement, optimize, and deploy machine learning models and pipelines for production. Perform data processing, feature engineering, model training, evaluation, and runtime tuning. Collaborate with engineers on integration and infrastructure, maintain technical documentation, participate in code reviews, and support sales/implementation teams with technical guidance.

Build and operate AWS-based MLOps platforms for autonomous-driving workloads. Maintain highly available training and deployment environments, distributed multi-GPU Ray clusters, Kubernetes infrastructure, Terraform code, Airflow and MLflow pipelines, and GitHub-based CI/CD. Troubleshoot platform issues and ensure machine-learning workflows are reproducible, traceable, and auditable.

The Senior Data Scientist will optimize demand forecasting models, apply machine learning techniques, and collaborate with teams to solve business problems.

Supports automation, robotics, and systems engineering projects within Moderna’s Applied Technologies team. Responsibilities include configuring and testing laboratory automation systems, executing experiments, troubleshooting, analyzing performance data, developing scripts and workflows, preparing technical documentation, evaluating prototypes, and presenting findings. The intern collaborates with engineers and scientists to improve reliability, throughput, usability, and scalability of technical development workflows.

Develop and deploy machine learning and statistical models for complex business problems. Responsibilities include data exploration, feature engineering, model training, validation, tuning, production deployment, pipeline collaboration, performance monitoring, MLOps, and communicating insights to technical and business stakeholders.

Drive adoption of Databricks Genie across a regional territory through technical strategy, executive engagement, workshops, presentations, demos, and customer-facing collateral. Partner with Account Executives, GTM, Product, Engineering, GSIs, and consulting firms. Architect governed analytics solutions spanning semantic models, warehouses, metric layers, Lakehouse, and AI ecosystems; address governance and security concerns; resolve complex technical escalations; and provide roadmap feedback.

Leads MSCI’s data operations transformation for private assets and ESG data. Responsibilities include managing teams, establishing data governance, improving quality and SLA performance, automating workflows with AI and technology, implementing cloud-native data acquisition solutions, and driving business process reengineering. The role collaborates with engineering, content, research, product, and project management teams; defines metrics and strategy; ensures compliance and resilience; and delivers scalable, efficient data operations.