Top Machine Learning Jobs in India

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.

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.

Lead end-to-end data science solutions for cyber fraud prevention, including machine learning models, anomaly detection, risk scoring, feature engineering, data pipelines, APIs, automated decisioning, and production monitoring. The role requires advanced Python and SQL, cloud and deployment experience, model validation, operational support, technical documentation, vendor evaluations, cross-functional collaboration, and mentoring of less-experienced team members.

Develop and maintain Python-based web and distributed applications that secure the firm's use of AI and Generative AI. Translate complex business requirements into technical designs, implement and test multi-tier applications, integrate AI and cloud APIs, and apply secure coding, privacy, and adversarial-risk controls. Collaborate with technology stakeholders, troubleshoot solutions, guide junior staff, and deliver software using Azure, containerization, DevOps, Agile, TDD, and BDD practices.

Work in a cross-functional team to develop, implement, and apply numerical and multi-physics models for materials, structures, and processes supporting memory development. Use scientific computing, computational materials methodologies, and ML techniques to analyze results, report findings, and collaborate on next steps. Position is temporary (up to 12 months).

Develops scalable automated testing frameworks and test suites for cloud and big-data platforms. Responsibilities include Python/PySpark automation, SQL and service testing, manual and performance testing, CI/CD deployment, monitoring utilities, integration testing, and GenAI automation. The role collaborates with development, infrastructure, operations, and client teams while providing technical guidance and maintaining test documentation.

Lead the architecture and technical direction of ServiceNow’s Voice AI platform, including real-time audio, conversational systems, agent orchestration, model integrations, and distributed systems. Drive scalable multi-region delivery, reliability, engineering standards, SRE capabilities, and cross-team technical alignment. Mentor senior engineers and develop technical leaders while shaping the platform roadmap with customers, product managers, and executives.

Build and scale distributed Voice AI systems, including audio acquisition, conversation management, speech generation, and integrations with hosted and self-hosted models. Partner with product managers and customers to develop enterprise AI solutions, write scalable production code, improve SRE tooling, and mentor engineers. The role requires architecting real-time voice platforms across regions and datacenters, developing LLM-based applications and agents, and operating highly available Kubernetes workloads.

Leads and scales Regeneron’s G&A Digital & Technology organization in Hyderabad. Oversees enterprise application development, integration, testing, release management, production support, and operational excellence across HR, Finance, Legal, Compliance, Analytics, AI, and other corporate functions. Builds global delivery capabilities through hiring, organizational design, talent development, governance, vendor management, and succession planning. Partners with senior business and technology leaders to establish roadmaps, improve reliability and quality, manage risk, and advance AI-enabled automation.

Leads execution of Labcorp.com website product priorities, evaluating capabilities, developing business cases, translating audience and business needs into product requirements, and coordinating pilots, proofs of concept, A/B tests, and implementation. Partners with Marketing Technology, IT, Analytics, Brand, Marketing, and Content teams to deliver emerging capabilities including conversational AI, intelligent search, personalization, automation, and adaptive digital experiences.

Owns and evolves Moderna’s global SAP S/4HANA Accounts Receivable and cash application ecosystem. The role configures AR, credit management, lockbox, electronic bank statements, clearing, banking integrations, e-invoicing, and payment platforms. It translates business requirements into functional specifications, leads testing and rollouts, ensures SOX compliance, and drives automation using AI, machine learning, and RPA. The position also provides global training, documentation, and support across Finance, Treasury, and Digital teams.

Reviews financial services regulations and exchange rulebooks, assesses regulatory changes, and maintains auditable review records. Validates machine-learning topic classifications, provides labelled feedback, and helps maintain regulatory taxonomies. Compares platform rulebooks with authoritative sources to identify structural errors, missing provisions, duplication, and versioning issues. Performs quality assurance against development requirements and collaborates with product, engineering, and software teams to process new regulations and improve the service.

Develop advanced supply chain analytics solutions using descriptive, diagnostic, predictive, and prescriptive modeling, machine learning, statistical analysis, forecasting, and operations research. The role involves gathering requirements, leading projects, managing stakeholders, deploying and sustaining models with DevOps/MLOps practices, presenting insights, and mentoring analysts in R, Python, and supply chain analytics. The position requires working a 1:30 p.m. to 10:30 p.m. shift.