Top Machine Learning Jobs in India

Lead enterprise AI initiatives from discovery through deployment, translating business requirements into technical roadmaps and delivering scalable software solutions. Collaborate with product, engineering, and data science teams in agile environments, lead technical assignments, manage stakeholders, promote secure coding and testing practices, and navigate governance requirements in a regulated environment.

Design and develop production-ready AI and machine learning applications using cloud AI services, on-premises pipelines, generative AI, deep learning, neural networks, chatbots, and image processing. Lead architecture and integration of emerging AI technologies, optimize pipelines for reliability and scalability, advise multiple teams, translate business requirements into AI solutions, and mentor junior engineers.

Designs and develops production-ready AI and machine learning applications using cloud and on-premises pipelines. Integrates generative AI, deep learning, neural networks, chatbots, and image processing into scalable solutions. Leads emerging technology evaluation, optimizes AI pipelines, advises multiple teams, translates business requirements into AI solutions, and mentors junior engineers.

Design and develop production-ready AI and machine learning applications using cloud and on-premises pipelines. Integrate generative AI models, deep learning frameworks, neural networks, chatbots, and image processing capabilities. Lead evaluation of emerging AI technologies, optimize AI pipelines, advise multiple teams, translate business requirements into solutions, make technical decisions, and mentor junior engineers.

Designs and builds custom AI infrastructure and hardware solutions for large-scale machine learning workloads. Optimizes computational stacks for performance, power consumption, cost, and scalability; evaluates vendors and integrates full technology stacks. Leads infrastructure strategy, cross-functional coordination, technical decisions, team mentorship, and knowledge sharing.

Assesses and implements cloud migration solutions while developing machine learning models and statistical analyses for business decision-making. Responsibilities include data preprocessing, segmentation, propensity modeling, recommender systems, experimentation, dashboard development in Looker, visualization, stakeholder communication, and documenting methodologies and data sources.

Designs and builds AI infrastructure and hardware solutions while optimizing performance, cost, power, and scalability. Conducts applied research in reasoning, planning, memory, retrieval, evaluation, adaptation, and multimodal AI; creates reproducible benchmarks; compares models; and converts validated techniques into production-quality services. Partners with architects and engineers on deployment, investigates production failures, mentors research engineers, and contributes to patents or publications.

Develop proofs of concept and production-oriented data science and AI solutions for government and organizational clients. Responsibilities include identifying use cases, preparing and assessing data, statistical modeling, forecasting, machine learning, LLM prototyping, evaluation, documentation, stakeholder communication, and client delivery. The role will establish data science capabilities for ICF’s India team and contribute to proposals, reusable processes, and project implementation.

Designs scalable architectures for automation frameworks, engineering tools, and intelligent systems. Leads architectural decisions, evaluates emerging technologies, develops prototypes, and guides teams toward production-ready solutions. Responsibilities include architecture reviews, technical standards, performance and reliability optimization, mentoring engineers, and collaboration with global stakeholders. The role also supports AI, generative AI, machine learning, distributed systems, and technology roadmaps across Ubisoft studios.

Leads enterprise AI/ML and mathematical optimization initiatives across healthcare, including solution architecture, hybrid ML-optimization modeling, generative and quantum-inspired AI, solver integration, MLOps, governance, and production deployment. Develops prototypes and reusable frameworks, guides technology adoption, mentors engineers, leads small teams, and partners with business, product, architecture, and engineering stakeholders to deliver scalable, compliant optimization solutions.

Leads enterprise AI strategy, architecture, and production delivery across generative, agentic, and classical machine learning. Designs LLM, RAG, orchestration, predictive, and advanced analytics solutions for healthcare and pharmacy operations. Establishes MLOps, LLMOps, governance, safety, monitoring, and evaluation frameworks while partnering with executives, product, engineering, and platform teams. Mentors technical practitioners and drives reusable AI standards, services, and accelerators.

Lead full-chip floor planning, custom analog and mixed-signal layout, physical implementation, verification signoff, and tape-out for advanced memory and high-speed interface designs. Own DRC, LVS, ERC, EMIR, reliability, and manufacturability closure. Define scalable layout methodologies, drive AI-enabled design automation, lead global cross-functional teams, mentor engineers, and deliver advanced-node semiconductor products.

Develops and supports machine-learning and advanced analytics solutions for Blue Yonder’s Luminate Planning suite. Responsibilities include feature selection, model development and evaluation, data mining, data cleansing, statistical analysis, forecasting validation, API and workflow integrations, and stakeholder presentations. The role also addresses customer data and forecasting concerns, mentors junior team members, and collaborates globally across implementation, product, support, and customer teams.

Build and validate rapid GenAI, VLM, and machine learning prototypes for game testing and quality control. Design experiments, develop data pipelines, evaluate model performance, integrate prototypes with internal tools, analyze failure modes, and document reproducible research practices. Collaborate in agile workflows with R&D and cross-functional teams while gaining experience in software development, testing, automation, CI/CD, and gaming technologies.

Own and enhance a sales crediting platform supporting incentive compensation. Build scalable SQL and Python data pipelines using Databricks, implement validation and reconciliation frameworks, troubleshoot production issues, and deliver analytics and dashboards. Partner with business, IT, and data stakeholders to translate requirements into technical solutions, automate processes, manage platform enhancements, and explore AI applications for anomaly detection, data quality, and operational efficiency.

Design, develop, and maintain enterprise-grade full-stack/backend systems; ship production-quality code to production; interact with customers and industry leaders to shape product direction; solve complex AI-native problems in the insurance money-movement domain. Requires strong CS fundamentals, analytic problem-solving, ownership mindset, and 4+ years experience.

Designs, develops, tests, and deploys machine learning models and workflows for audience measurement. Builds data pipelines using Databricks and AWS S3, cleans complex data sources, applies statistical and econometric methods, and maintains production-quality Python, PySpark, and SQL code. Creates Tableau visualizations, communicates research findings, and collaborates with Engineering, Product, Data Science, and Technology stakeholders to deliver data-driven improvements.

Maintains, optimizes, and supports enterprise applications and infrastructure. Troubleshoots application issues, implements software deployments and configuration changes, supports onboarding and decommissioning, and contributes to systems recovery. Configures packaged software, develops modification and interface requirements, coordinates solutions with technical teams and vendors, executes change requests, mentors junior staff, and provides strategic recommendations.

Develop machine learning prediction models across customer acquisition, behavior, collections, marketing, fraud, and propensity use cases. Design data-driven strategies for banking products, leverage digital and alternative data, collaborate with external data providers, and lead large-scale model development and deployment. Own the full machine learning lifecycle, including feature engineering, training, validation, documentation, deployment, monitoring, and feedback loops.

Develop and test AI agents alongside engineers and researchers. Write clean, efficient, documented code; debug software; collaborate with the team; and stay current with AI advancements. The role provides hands-on experience with AI tools and technologies, training, and access to modern AI resources. Strong Python or JavaScript programming skills, machine learning and deep learning knowledge, problem-solving ability, and teamwork are required.

Conduct experimental research in machine intelligence, develop and refine machine learning algorithms and models, apply research findings to existing AI systems, collaborate with engineers and researchers, publish results, and stay current with advances in machine learning.

Performs vehicle data mining, exploratory analysis, event triage, and root-cause analysis using Python and data-science tools. Supports AI/ML validation for ADAS features, including dataset assessment, labeling, metrics review, and data-quality investigation. Analyzes CAN, Ethernet, ECU logs, and vehicle signals; assists with embedded debugging, instrumentation, vehicle testing, and data collection. Collaborates with algorithm, systems, validation, and engineering teams while following ADAS development and documentation processes.

Develop internal engineering tools, platforms, automation, dashboards, telemetry, and AI-powered workflows for Power BI Desktop and Service. Build secure, scalable cloud-native systems, modernize legacy architectures, improve release validation and observability, and integrate GenAI and machine learning into developer workflows. Collaborate globally with product, QA, release engineering, infrastructure, and partner teams to improve engineering productivity, quality, compliance, and delivery insights.

Develop and lead scalable Java full-stack software solutions for SaaS and enterprise environments. Responsibilities include technical design, coding, testing, debugging, architecture and code reviews, maintaining codebases, improving system quality, and supporting customers. The role requires leading complex projects, mentoring developers, collaborating across functions, evaluating technologies, and building highly scalable web services and distributed cloud systems using Java, AWS or Azure, CI/CD, databases, and big data technologies.

Develop and enhance scalable Java enterprise software and distributed systems. Create technical designs, implement and test robust web services, maintain codebases, conduct architecture and code reviews, troubleshoot production issues, and support cloud-based SaaS infrastructure. Lead technical direction, mentor developers, collaborate cross-functionally, and drive architecture, design, and implementation for mission-critical, low-latency solutions using cloud, big data, and DevOps technologies.