Top Machine Learning Jobs in India

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 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.

Lead development of scalable Java enterprise and SaaS solutions, including architecture, design, implementation, testing, debugging, and maintenance. Guide technical direction, conduct architecture and code reviews, mentor developers, improve system quality, and collaborate with product, customer support, and cross-functional teams. The role requires expertise in distributed systems, cloud infrastructure, CI/CD, big data, web services, software security, and modern testing practices.

Lead the strategy, growth, transformation, and scaling of LSEG’s Broker Models business. Responsibilities include expanding coverage and adoption, modernizing data collection and delivery through AI and automation, developing scalable operating models, translating customer needs into product capabilities, driving commercial outcomes, and building global cross-functional teams across Product, Engineering, Data Science, Operations, and Commercial functions.

Lead hands-on applied AI development in fintech, taking computer vision or language models from research through production and ongoing optimization. Own technical decisions across model selection, fine-tuning, prompting, cost, latency, accuracy, evaluation, data strategy, retraining, and incident resolution. Mentor engineers and convert ambiguous business problems into sequenced roadmaps. Preferred experience includes fintech applications such as KYC, fraud, risk, document intelligence, scalable GPU infrastructure, and regulated model governance.

Lead confidential executive searches for senior technology, product, sales, and engineering leaders. Partner with executives and HR stakeholders to define hiring strategies, build talent maps and passive-candidate pipelines, assess leadership capabilities, manage complex interviews and offers, and provide market intelligence. Track executive hiring metrics and improve recruitment outcomes through data, AI, and scalable processes while delivering a high-touch candidate experience.

Develop and deploy advanced numerical simulations for aircraft structural design, certification, and in-service support. Perform linear and nonlinear finite element analysis, assess static and dynamic strength, identify design issues, support certification testing, investigate root causes, and optimize manufacturing processes. The role also develops credible simulation methods, automates simulation workflows, and explores AI and machine learning to improve simulation capabilities.

Leads enterprise data engineering, AI, and cloud initiatives, including platform modernization, migration, integration, analytics, and data services. Manages technical delivery plans, risks, governance, resources, Agile execution, and engineering standards. Coaches data, cloud, and AI teams while partnering with stakeholders, architects, product owners, and security teams. Drives platform reliability, cost optimization, incident resolution, operational excellence, and scalable solutions using Databricks, Snowflake, cloud platforms, and modern DataOps practices.

Leads a BI and analytics team supporting operational analytics across pharmaceutical development and supply functions. Responsibilities include designing data pipelines, models, dashboards, and reports; analyzing complex datasets; deploying scalable analytics and AI solutions; partnering with IT and business stakeholders; ensuring governance and data security; automating high-value analytics; and mentoring analysts.

Build and optimize scalable machine learning models for fraud detection using large, complex datasets. Analyze fraud patterns, develop classification and regression models, troubleshoot deployed solutions, create visualizations, and communicate insights to technical and non-technical stakeholders. Collaborate with engineers and business teams on model deployment, continuous improvement, and innovation initiatives.

Lead the vision, strategy, roadmap, and execution of eBay’s AI-powered Risk Decision and Insights platform. Gather user requirements, drive platform adoption, align senior risk leaders, and coordinate global matrixed teams. The role requires deep product management expertise across risk, payments, or ecommerce, platform development experience, knowledge of fraud decisioning or machine learning infrastructure, and the ability to manage complex strategic products through the full development lifecycle.

Builds and optimizes AI Operations solutions for IT processes, including incident management, root cause analysis, monitoring, anomaly detection, and infrastructure problem resolution. Develops and deploys machine learning models, AI agents, and CI/CD pipelines while integrating AI solutions with existing infrastructure. Collaborates with data science, software engineering, and operations teams to improve reliability, scalability, performance, and operational efficiency.

Develop statistical and machine learning models to optimize Akamai’s global mapping service. Analyze massive telemetry streams, build predictive observability tools and automated data pipelines, define reliability metrics, investigate outages, and guide architecture and technology investment decisions. Collaborate with product engineers and communicate complex quantitative insights to technical and executive stakeholders.

Conduct AI risk, governance, responsible AI, and assurance assessments across enterprise environments. Evaluate generative AI, machine learning systems, AI agents, platforms, and third-party services for security, privacy, regulatory, ethical, and operational risks. Assess AI lifecycle controls, architectures, model performance, fairness, explainability, and oversight. Partner with technical, legal, compliance, and business teams, facilitate client workshops, and deliver risk assessments, executive reports, methodologies, and practical recommendations.

Client-facing healthcare analytics business analyst responsible for engaging senior executives, identifying business pain points, designing demonstrable analytics solutions, supporting consultative sales, and helping shape product roadmaps. The role requires deep U.S. healthcare knowledge, claims and interoperability expertise, hands-on analytics experience, stakeholder communication, requirements documentation, product collaboration, and mentoring. Work spans healthcare data management, BI, cloud, AI, machine learning, and emerging analytics services.

Owns end-to-end product management for Supply Chain Management data and analytics solutions. Translates business needs into scalable solution designs, coordinates data, BI, ML, AI, engineering, architecture, governance, and DevOps teams, and manages backlogs through delivery, testing, validation, go-live, and continuous improvement. Serves as the primary interface between business stakeholders and IT while ensuring compliance, adoption, performance, and ongoing support.

Leads physical implementation of next-generation CPU IPs from floorplanning through signoff. Oversees placement, clock tree synthesis, routing, timing and physical verification, while driving power, performance, and area optimization. Partners with architecture, RTL, CAD, DFT, power, and signoff teams to resolve complex implementation challenges, improve methodologies, adopt automation and AI-driven workflows, mentor engineers, and support planning, hiring, and execution tracking.

Develop and deploy automation frameworks for physical design signoff across RC extraction, STA, ERC, power integrity, and signal integrity. Automate checks, debugging, reporting, and data analysis; resolve signoff issues; improve methodology, convergence, quality, and correlation; collaborate with design, CAD, and EDA vendor teams; and build dashboards to monitor signoff health and project readiness.

Develop AI/ML algorithms for semiconductor manufacturing by combining physics-based modeling, machine learning, and systems engineering. Responsibilities include building sensor-data pipelines, predictive and control models, physics-informed neural networks, reduced-order models, and production software integrated with manufacturing tools, control systems, and dashboards. The role analyzes metrology data, optimizes model performance, and collaborates across physics, engineering, software, and data science teams.