Top Machine Learning Jobs in India

Lead the end-to-end lifecycle for AI-powered and Agentic AI products, defining roadmaps, requirements, objectives, and product goals. Partner with engineering, design, research, sales, and external stakeholders to deliver solutions across complex commercial and manufacturing environments. Translate customer needs and emerging AI trends into product opportunities, manage tradeoffs, and drive iterative launches. The role requires extensive product management experience, AI/ML understanding, agile development expertise, and cross-functional leadership.

Build and maintain controllership reporting, analytics, data pipelines, dashboards, and automation solutions using SQL, Python, Alteryx, Databricks, ThoughtSpot, and UiPath. The role supports financial reporting, data quality, governance, reconciliation, and executive insights while documenting data models and ensuring production reliability. Responsibilities include leading complex analytics initiatives, translating stakeholder needs into scalable solutions, optimizing performance, and mentoring team members across Controllers, Finance, and technology teams.

Leads strategy, delivery, and business outcomes for AI-powered claims, payment, reimbursement, and member-experience products. Partners with engineering, architecture, AI, design, analytics, and business teams to define roadmaps, technical requirements, integrations, KPIs, and operational controls. Oversees scalable web, mobile, and conversational experiences in a regulated healthcare environment while coaching Technical Product Managers and advancing AI-enabled product practices.

Analyzes healthcare business and system requirements, creates functional specifications and process documentation, supports implementations and integrations, troubleshoots production issues, performs testing and defect triage, and identifies workflow improvements. The role collaborates with technical and business stakeholders on Agile healthcare technology initiatives, cloud modernization, regulatory compliance, healthcare data analysis, and payer-related systems.

Develop enterprise-wide AI strategy, align CXO and business stakeholders, and lead innovation forums from ideation through implementation. Manage AI and transformation roadmaps, establish metrics for scaling AI, improve processes through AI, and coordinate operations, data, technology, and transformation teams. The role also requires change management, execution readiness, stakeholder leadership, and strong communication and presentation skills.

Develop and deploy production-grade NLP, machine learning, deep learning, and generative AI solutions for healthcare. Responsibilities include data preparation, feature engineering, modeling, optimization, model lifecycle management, RAG implementations, scalable architecture, proof-of-concepts, performance monitoring, documentation, project leadership, research, and mentoring junior engineers.

Leads the design, development, deployment, and optimization of enterprise AI/ML solutions, including generative AI, LLMs, RAG pipelines, agentic workflows, chatbots, and predictive analytics. Owns architecture, production operations, MLOps, monitoring, security, compliance, and responsible AI practices. Builds full-stack AI services using Azure, Python, Java, Spring Boot, React, and APIs; mentors engineers and communicates technical decisions to stakeholders.

Lead the design and deployment of machine learning, statistical, and analytics solutions for fraud detection across travel, payments, and expense workflows. Develop fraud models, anomaly detection, graph analysis, rules, and real-time monitoring systems. Own the model lifecycle, conduct loss attribution and root-cause analyses, collaborate with engineering and product teams, mentor junior staff, and shape fraud strategy and roadmap.

Develop centralized semiconductor quality screening and lot-disposition systems, assess wafer quality and reliability risks, analyze manufacturing data, and create statistical monitoring and automation solutions. The role supports defect containment, CAPA, global quality standardization, dashboards, AI and machine-learning initiatives, audits, and continuous improvement across Micron’s worldwide wafer-fabrication network.

Lead the globally distributed Insight Master Data Management organization, overseeing people, processes, service delivery, data quality, controls, onboarding, integrations, and migrations. Drive scalable operating models, automation, and continuous improvement while partnering with Product and Engineering to evolve MDM capabilities. Develop team expertise, establish performance metrics, resolve operational issues, and translate business needs into technology and process priorities.

Develops and maintains forecasting models and processes for Home Lending Finance, supports budgeting and forecasting cycles, migrates legacy models, documents regulatory processes, conducts UAT, performs ad hoc analysis, and communicates insights to senior stakeholders. The role requires Python, SQL, econometric modeling, machine learning familiarity, database platforms, project management, and strong financial analysis capabilities.

Develop advanced data science algorithms, apply statistical and machine learning techniques to large datasets, evaluate emerging data technologies, and build scalable generative AI and deep learning solutions. Collaborate with clients and engineering teams to communicate findings, deploy models into production, and maintain reliable workflows using software engineering best practices.

Leads complex validation and risk assessment of fraud detection and prevention models within Model Risk Management. Responsibilities include challenging model development, implementation, documentation, statistical and machine learning analysis, monitoring emerging fraud risks, preparing technical reports, defining review scope, and advising on analytical strategies. Collaborates with regulators, auditors, and stakeholders while presenting findings and supporting risk mitigation decisions.

Leads complex quantitative analytics, statistical modeling, forecasting, and model risk management for fraud and financial risk models. Responsibilities include assessing market, credit, and operational risks; computing losses and capital requirements; validating models throughout their lifecycle; producing technical reports; developing validation tools and prototypes; and advising stakeholders, regulators, auditors, and the broader model risk community on governance, automation, and best practices.

Leads technical programs for digital payment transformation and transaction authorization optimization. Responsibilities include product backlog management, requirements prioritization, project planning, product governance, operational support for AI-driven recommendations, and cross-functional delivery with engineering, data science, operations, and design teams. The role also develops product roadmaps, business cases, success metrics, and scalable integration solutions while supporting data security, compliance, architecture reviews, and stakeholder alignment.

Develops analytics and AI-driven tools for pre-sales value quantification. Manages large datasets using SQL, performs statistical analysis, and creates dashboards in Tableau or Power BI. Builds ROI calculators and web-based solutions, generates actionable insights, and collaborates with Sales, Marketing, Consulting, and Product teams to support customer engagement and strategic decisions.

Leads the architecture and implementation of scalable data platforms, pipelines, ETL/ELT workflows, and cloud-native AWS data systems. Builds and optimizes Python, SQL, and Airflow pipelines; manages data warehouses, modeling, governance, quality, lineage, and security. Provides technical leadership and mentorship while collaborating with analytics, product, engineering, and business teams. Develops reliable, cost-efficient, highly available data infrastructure and translates business needs into scalable technical solutions.

Lead the design, development, and maintenance of scalable .NET microservices across Azure and AWS. Establish CI/CD, automated testing, infrastructure as code, and cloud optimization practices. Diagnose application issues, improve scalability and availability, and provide technical leadership through architecture, code reviews, design documentation, mentoring, and proof-of-concept development. Build solutions using Kubernetes, Cosmos DB, Azure services, modern front-end frameworks, AI tools, and RESTful APIs.

Lead product strategy and roadmap development for ServiceNow’s AI Agent Platform. Prioritize features, create technical AI proofs of concept, collaborate with engineering, design, and AI research teams, and demonstrate solutions to customers and internal stakeholders. Translate customer requirements into functional solutions, define KPIs, track outcomes, and communicate recommendations on emerging AI and platform technologies.

Leads the end-to-end development, productization, deployment, and lifecycle management of AI, ML, GenAI, and agentic AI solutions for autonomous network operations. Owns AI product roadmaps, architectures, MLOps, adoption metrics, customer value, and scalable deployment frameworks. Partners with customers, product and engineering teams, mentors AI professionals, and drives innovation across telecommunications networks, including 5G, 6G, RAN, Core, Transport, and OSS/BSS.

Develop predictive models, machine learning solutions, business intelligence capabilities, and analytical programs using R and Python. Analyze large datasets, conduct scorecard and portfolio analytics, generate recommendations, support client and product initiatives, and present findings to stakeholders. The role also involves data extraction, segmentation, documentation, performance monitoring, and collaboration with project managers and analytics teams.

Execute T+1 control and AFR reconciliations, investigate and resolve discrepancies, coordinate with technology and business stakeholders, maintain reconciliation data and logic, escalate unresolved issues, and produce recurring reports. The role supports trade processing from order entry through settlement while improving operational efficiency, data quality, and process resilience.

Analyze complex datasets using advanced analytics, data mining, SQL, and visualization tools to generate actionable business insights. Partner with stakeholders and senior leaders to guide strategy, improve product performance, identify opportunities, and solve quantitative business problems. Develop data models and analytics strategies, assess data quality, identify anomalies, communicate findings clearly, and independently manage analytical programs and process improvements.

The Staff Data Analyst will transform data into insights for GitLab’s Support Engineering organization. Responsibilities include capacity and budget modeling, utilization scorecards, support economics analysis, performance indexes, AI adoption ROI dashboards, predictive churn analytics, customer-schema standardization, executive reporting, and ad hoc analysis. The role partners with Support Engineering, go-to-market, Business Operations, and Product Engineering in a remote environment.

Principal Engineer responsible for enterprise HR data engineering strategy, architecture, modernization, and delivery. Leads migration to cloud-native lakehouse platforms, designs scalable data pipelines and frameworks, and provides hands-on technical leadership across Azure and GCP. Drives AI and Generative AI adoption, governance, observability, data quality, and responsible implementation. Advises senior leadership, mentors engineering teams, and aligns secure, resilient technology solutions with business, risk, and regulatory objectives.