Top Machine Learning Jobs in India

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.

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.

Build and maintain modular, configurable, API-first internal product creation tools for Nike’s Innovation and Consumer Creation teams. Develop cloud-native software and modern web applications, collaborate with engineers and product leaders, and apply architectural design patterns, testing methodologies, CI/CD, and infrastructure-as-code practices. Work with AWS services, frontend frameworks, REST services, browser APIs, and potentially AI and machine learning solutions.

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.

Leads the architecture, development, deployment, and support of Python-based AI applications. Builds and improves machine learning, NLP, deep learning, and generative AI solutions, including LLM, RAG, agentic AI, embedding, and vector database applications. Establishes evaluation and observability frameworks, creates reusable data science APIs and packages, drives adoption of emerging technologies, ensures scalability and stability, and mentors technical team members.

Designs, builds, deploys, and supports scalable batch and real-time data pipelines. Prepares and integrates structured and unstructured data, implements data quality controls, investigates defects, and maintains metadata, lineage, master data, and governance. Develops reusable data assets and automated workflows supporting analytics, business intelligence, data science, and AI use cases. Collaborates with technology and business teams to translate requirements into reliable solutions while applying data privacy, security, and responsible AI practices.

Designs and maintains enterprise data pipelines, integration processes, analytical assets, and custom data solutions. Performs data profiling, quality analysis, cleansing, standardization, and remediation while automating manual workflows and improving scalability. Supports data science and advanced analytics initiatives, applies responsible AI practices, and develops semantic models using tools including Databricks, Tableau, Power BI, Alteryx, and PowerApps. Coaches junior colleagues and partners with stakeholders to translate technical solutions into business value.

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.

Leads data science, AI, and machine learning capabilities across enterprise platforms. Develops cloud-based content classification systems, governs content lifecycle and metadata enrichment, embeds AI intelligence into product roadmaps, and creates agentic AI frameworks. Partners with product, engineering, and domain teams to align technical execution with business goals while mentoring operations and enablement teams.

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.

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.