Top Machine Learning Jobs in India

Owns the strategy, roadmap, delivery, and adoption of enterprise AI enablement platforms and AI products. Responsibilities include defining AI capabilities, prioritizing use cases, managing product lifecycles, partnering with engineering and data teams, establishing responsible AI governance, managing budgets and vendors, and influencing senior stakeholders. The role also mentors product teams and drives AI adoption, evaluation, monitoring, compliance, and organizational transformation.

Leads clinical data science and statistical analysis across pharmaceutical development studies. Designs analyses, predictive models, simulations, and integrated data reviews using clinical, biomarker, real-world, and external data. Oversees analysis plans, data quality, vendors, regulatory submissions, and reproducible workflows while applying AI/ML and CDISC standards. Partners cross-functionally, mitigates delivery and regulatory risks, mentors colleagues, and advances data science capabilities through automation, standards, publications, and external collaboration.

Analyzes, audits, designs, and optimizes telecom networks across RAN, transport, core, operations and maintenance, and services domains. Supports pre-sales and service delivery by gathering customer requirements, defining scopes of work, estimating costs, developing service proposals, and presenting value propositions. The role also addresses technical challenges, recommends solutions, applies automation and machine learning tools, and manages customer and stakeholder collaboration.

Owns enterprise AI platform architecture at global telecom scale, spanning cloud-native ML/LLM platforms, MLOps, RAG, vector databases, knowledge graphs, and agentic AI. Defines reference architectures, governs production delivery, leads architecture reviews, and establishes standards for model operations, security, observability, compliance, reliability, and cost. Designs multi-cloud inference, self-hosted and commercial model integrations, agent orchestration, and secure integrations with operational systems. Requires extensive software engineering and architecture experience, advanced AI/ML expertise, and strong cloud, security, and enterprise architecture skills.

Lead the design, deployment, and production scaling of machine learning, statistical, LLM, and agentic AI solutions. Build RAG applications, multi-agent systems, model evaluation frameworks, and reusable AI tools. Partner with engineering and product teams to identify opportunities, prepare datasets, improve model reliability and fairness, and guide initiatives from problem definition through continuous improvement.

Leads regional order-to-cash product integration across order capture, fulfillment, billing, collections, cash application, and customer care. Owns business requirements, portfolio visibility, governance, BPO performance, digital adoption, and value realization. Establishes a regional community of practice, aligns requirements with enterprise roadmaps, tracks Celonis KPIs, and holds product and operations leaders accountable. Requires strong OTC, SAP, Celonis, portfolio governance, financial, analytical, and executive stakeholder-management expertise.

Leads enterprise software testing and quality engineering strategy across multiple global teams. Oversees automation frameworks, performance and resilience testing, AI-assisted testing, CI/CD integration, synthetic testing, quality metrics, and production issue escalation. Mentors engineering leaders, establishes governance and operating standards, and partners with senior stakeholders to improve reliability, delivery speed, security, and engineering quality.

Design, develop, deploy, and maintain production machine learning and GenAI solutions at scale. Own ML features through the full lifecycle, including system and API design, testing, monitoring, troubleshooting, and operational support. Collaborate with engineering, product, design, and data teams to deliver reliable, secure, measurable outcomes while improving ML application performance, maintainability, and customer value.

Leads strategy, roadmaps, delivery, and business outcomes for a portfolio of AI and ML products. Manages and develops technical product managers, oversees AI lifecycle quality and governance, partners with engineering and data teams, drives customer-focused product strategy, and ensures responsible delivery in a regulated healthcare payer environment.

Leads software and AI/ML engineering strategy, architecture, delivery, and organizational development. Manages multi-team engineering organizations, roadmaps, budgets, hiring, and leadership development while overseeing cloud-native services, APIs, machine learning, generative AI, RAG, AI agents, MLOps, LLMOps, responsible AI, security, reliability, and production operations. Partners with product, data, security, and business leaders to deliver measurable outcomes and accelerate modernization.

Leads the design, development, and delivery of complex AI-native and cloud-native systems across frontend, backend, and AI engineering. Builds LLM evaluation infrastructure, retrieval systems, guardrails, testing, and observability. Drives architecture, technical initiatives, quality standards, and platform improvements while mentoring engineers and collaborating with product managers. Makes trade-offs involving scalability, reliability, latency, cost, and model quality, and owns solutions through implementation, testing, and production delivery.

Develop fraud prevention strategies, analyze customer transactions and fraud patterns, apply AI and machine learning to improve detection, align policies with risk teams, monitor regulatory and technology changes, and mentor colleagues on fraud prevention procedures.

Develops statistical, machine learning, and data analytics solutions for supply chain and spare parts management. Responsibilities include collecting and preprocessing data, building predictive and prescriptive models, anomaly detection, recommendation engines, visualizing insights, supporting data-driven decision-making, communicating findings to stakeholders, exploring AI developments, and mentoring colleagues. Requires supply chain knowledge, strong analytical and statistical skills, Python and SQL experience, hands-on machine learning implementation, and cloud-based ML experience.

Own the Fraud & Risk product roadmap, strategy, and metrics across merchant onboarding, payments, payouts, and disputes. Partner with engineering, data science, operations, compliance, legal, and payments teams to develop fraud detection, risk decisioning, AI automation, and chargeback solutions. Balance fraud loss prevention, customer friction, growth, and operational efficiency while delivering compliant, scalable products.

Leads Toast’s Bengaluru embedded finance underwriting operations, building the function, processes, controls, tooling, and team from the ground up. Manages underwriting decisions and escalations, recruits and develops staff, drives automation and AI-enabled workflows, establishes performance analytics and BI dashboards, partners with Product, Engineering, and Risk, and maintains credit policy, regulatory, and compliance standards across global teams.

Own the APAC onboarding and KYC product strategy, roadmap, and delivery across multiple markets. Lead identity verification, localization, regulatory controls, and financial crime mitigation initiatives. Partner with engineering, design, analytics, operations, risk, and compliance teams to improve conversion, verification speed, manual review rates, cost-to-serve, and decision quality. Use customer insights, operational feedback, data, and regulatory context to prioritize and launch scalable product capabilities.

Build and scale machine learning systems for search, including ranking models, feature engineering, model deployment, monitoring, experimentation, and online inference. Design fault-tolerant distributed infrastructure, optimize search latency and throughput, and develop reliable ML services from proof of concept through production. Collaborate across engineering and product teams, communicate technical decisions, review code, document systems, and mentor junior engineers.

Leads software engineering squads delivering scalable, high-performing products. Responsibilities include architecture and system design, full-stack development, prioritization, cross-functional coordination, mentoring, quality and security oversight, automation, testing, root-cause analysis, operational excellence, and continuous improvement across the software development lifecycle.

Leads the strategy, architecture, delivery, and operationalization of agentic AI solutions in enterprise healthcare environments. Manages engineering teams building LLM workflows, copilots, intelligent automation, and integrated AI platforms. Responsibilities include cloud architecture, secure deployment, AI evaluation, stakeholder alignment, delivery planning, technical governance, executive communication, and coaching engineering leaders. The role also drives scalable engineering practices and production-ready solutions across marketing technology deployments.

Leads a team of data scientists and AI/ML professionals developing predictive and prescriptive models for healthcare payment integrity. Oversees the full model lifecycle, including data exploration, feature engineering, validation, deployment, monitoring, governance, and optimization. Partners with senior stakeholders to identify fraud, waste, abuse, billing anomalies, overpayments, and leakage opportunities. Promotes responsible AI, model risk management, innovation, and production adoption while mentoring teams and delivering measurable business outcomes.

Leads a semiconductor RDA team focused on defect detection, process anomaly identification, inspection technology development, data analysis, and continuous improvement for NAND technology development. Partners with process integration, data science, yield analysis, suppliers, and global manufacturing teams to develop inspection roadmaps, improve detection techniques, manage projects, establish program metrics, and transfer best-known methods across sites. Mentors engineers and drives root-cause analysis, innovation, and technical problem solving.

Design and implement ML solutions (predictive models, recommender systems, anomaly detection, deep learning). Optimize technical requirements, build cloud and on-prem ML pipelines, collaborate with business stakeholders, and mentor junior data scientists.

Leads AI/ML strategy, technical vision, and delivery of scalable machine learning platforms for map editing and maintenance. Drives generative AI, agentic AI, computer vision, LLM, automation, MLOps, governance, observability, security, and responsible AI adoption. Mentors data scientists, leads multidisciplinary initiatives, translates business needs into measurable solutions, and influences executives, customers, vendors, and partners.

Develop predictive and machine-learning models that quantify product, service, and customer value for Mastercard’s pricing and commercialization strategies. Build value-based B2B pricing models, forecasts, business cases, elasticity analyses, and revenue scenarios. Analyze complex datasets using Python and SQL, translate findings into commercial insights, and influence product, pricing, data science, and senior stakeholder decisions.

Designs data-driven digital products, prototypes, dashboards, applications, chatbots, and interfaces using human-centered design, Lean UX, and Design Thinking. Leads workshops, develops personas and user journeys, creates visual and interaction designs across web, mobile, and IoT, and collaborates with product, technology, marketing, and regional teams. Conducts iterative user testing, contributes to design systems, and shapes AI- and ML-enabled experiences while balancing usability, business goals, regulatory compliance, and global user needs.