Top Machine Learning Jobs in India

Leads strategy, roadmap, and execution for AI-powered help experiences, AI agents, and composable platforms supporting regulatory compliance and trusted commerce. Partners with engineering, design, data science, legal, policy, compliance, risk, and operations teams to translate customer and regulatory needs into scalable products. Uses data, experimentation, and customer insights to improve experiences, manage risk, and deliver reliable, explainable AI capabilities across global markets.

Lead development of ARM-based SoC subsystems using Cadence design, verification, and emulation platforms. Build and port systems to Palladium and Protium, ensure functional correctness, analyze hardware-software performance, and recommend improvements. The role requires deep RTL development and verification experience, expertise in AMBA and CHI protocols, SoC integration knowledge, and emulation experience. Familiarity with PCIe, CXL, and automated integration and verification methodologies is beneficial.

Leads and scales Databricks’ AI Forward Deployed Engineering team in India. Responsibilities include hiring and mentoring AI/ML talent, leading enterprise AI transformation engagements, managing strategic customer relationships, shaping product and go-to-market strategy, overseeing AI services accounts and revenue, developing scalable delivery practices, and representing Databricks as an AI thought leader. The role requires strong AI/ML technical depth, executive stakeholder management, production AI experience, and monthly travel.

Develop and deploy machine learning systems for multivariate anomaly detection, correlation analysis, causality discovery, and complex time-series pattern recognition. Responsibilities include real-time monitoring, adaptive thresholding, automated alert prioritization, correlation-based clustering, dashboards, model performance tracking, and production integration. The role requires strong statistics, time-series analysis, ML engineering, distributed data processing, visualization, and MLOps experience.

Develop and lead production AI/ML solutions for healthcare, including NLP, deep learning, generative AI, forecasting, and computer vision. Manage the full machine learning lifecycle from data preparation and feature engineering through deployment, monitoring, and documentation. Optimize and quantize generative AI models, build proof-of-concepts, establish development best practices, and collaborate with research, engineering, and product teams. Promote ethical AI through fairness, transparency, and accountability.

Develop and deploy advanced AI, machine learning, deep learning, NLP, and computer vision solutions for healthcare. Lead end-to-end model development, including data preparation, feature engineering, modeling, deployment, monitoring, documentation, and lifecycle management. Optimize generative AI models, apply RAG and agentic AI techniques, conduct complex proofs of concept, establish development best practices, and collaborate with research, engineering, and product teams while promoting ethical AI.

Design, build, and productionize large-scale machine learning, generative AI, and agentic systems. Own the full AI product lifecycle from experimentation through deployment, implementing MLOps workflows with MLflow and Databricks for reliability, scalability, monitoring, and performance tracking. The role requires production model deployment experience, expertise in Databricks technologies, and familiarity with agent development frameworks such as LangChain, LangGraph, or DSPy.

Develops and deploys production machine learning, deep learning, NLP, generative AI, and MLOps solutions. Responsibilities include model development, feature engineering, validation, RAG and LLM applications, model monitoring, CI/CD, cloud deployment, data analysis, visualization, and AI research. The role collaborates with stakeholders to identify business problems and deliver scalable, governed AI systems.

Analyze product, customer, and business data to identify trends, risks, and opportunities. Build dashboards, metrics, reports, analytics solutions, and predictive models while supporting data pipelines and AI-powered automation. Partner with Product Managers, Engineers, customers, and business stakeholders to define requirements, improve workflows, validate product ideas, and measure outcomes. Own initiatives from problem identification through delivery while developing expertise in product management, data engineering, AI, and enterprise financial technology.

Lead enterprise AI architecture for generative AI, agentic AI, and machine learning engagements. Design scalable production systems, advanced RAG and retrieval platforms, multi-agent workflows, evaluation and observability standards, and responsible AI controls. Remain hands-on with Python, Vertex AI, model tuning, and agent frameworks. Advise executive clients, lead discovery and pre-sales, guide engineering teams, and drive initiatives from experimentation through production and measurable business outcomes.

Supports settlement risk management across payment clearing and settlement activities. Responsibilities include monitoring credit, liquidity, operational, concentration, and participant default risks; assessing exposures, limits, collateral, and breaches; developing risk policies, dashboards, reports, and governance materials; supporting stress testing and control assessments; tracking remediation actions; and providing independent oversight and challenge to risk owners. The role also analyzes risk data, supports GRC tool implementation, monitors regulatory developments, and contributes to AI-enabled risk monitoring.

Leads the design and development of scalable AI solutions, serving as a technical subject matter expert for complex projects. Responsibilities include collaborating with product and business teams, managing the end-to-end AI development lifecycle, mentoring AI engineers, allocating resources, and ensuring technical excellence. The role also involves applying machine learning methodologies, integrating AI into existing systems, and driving cloud development initiatives.

Build scalable, low-latency backend APIs, event-driven services, integration layers, and microservices supporting personalization and recommendation experiences. Partner with AI/ML and data engineers to deploy models, expose AI services, and develop React applications. Improve security, observability, reliability, performance, scalability, and operational efficiency while using AI-assisted engineering tools to enhance delivery and code quality.

Lead complex global supply chain planning implementations and consulting engagements. Architect scalable solutions using BY ESP and comparable platforms, guide customer solutioning discussions, manage executive stakeholders, and deliver measurable transformation outcomes. Apply supply chain domain expertise, data models, cloud architectures, integrations, and AI-enabled planning approaches to improve deployment speed and customer adoption.

Build and own scalable AI and Generative AI platforms using Python, microservices, cloud infrastructure, and machine learning models. Design backend architecture, deploy and optimize ML systems, manage Kubernetes and Terraform-based infrastructure, maintain databases and observability platforms, and lead production reliability practices. The role also involves technical leadership, mentoring, architecture decisions, incident response, and translating client requirements into robust technical solutions.

Design, build, test, deploy, and maintain innovative distributed software systems. Lead technical direction and feature roadmap for a critical international expansion function, optimize service performance and security, and operate in an agile environment. The role includes significant autonomy, cross-functional communication, and opportunities to hire and shape the engineering team. Candidates should have strong computer science fundamentals, modern software development experience, and familiarity with cloud, databases, frontend frameworks, machine learning, and DevOps technologies.

Leads advanced data science projects using machine learning, statistical modeling, and data analysis to deliver business insights. Serves as a technical subject matter expert, translates business needs into analytical solutions, mentors junior data scientists, manages end-to-end projects, and contributes to the team’s strategic direction and methodological improvement.

Leads advanced data science projects using machine learning, statistical modeling, and data analysis. Translates business needs into analytical solutions, manages end-to-end projects, mentors junior data scientists, develops algorithms, addresses complex data challenges, and contributes to the strategic direction of the Data Science team.

Leads the strategy, design, development, and deployment of Generative AI and LLM solutions for software and platform clients. Responsibilities include identifying AI opportunities, developing roadmaps, managing cross-functional projects, overseeing model development and integration, managing client relationships, identifying expansion opportunities, and mentoring AI professionals. The role also manages project scope, resources, risks, timelines, budgets, and quality standards.

Develop and optimize computer vision, machine learning, deep learning, and image processing algorithms for semiconductor inspection and metrology systems. Responsibilities include defect detection, segmentation, registration, classification, benchmarking, validation, performance optimization, and large-scale data analysis. The role collaborates with product, software, HPC, applications, and systems teams to integrate algorithms into production software and resolve customer issues.

Leads a technical team developing computer vision, machine learning, image processing, and deep learning algorithms for semiconductor inspection and metrology systems. Defines architecture and technical roadmaps, drives high-performance CPU/GPU implementations, oversees validation and product releases, resolves customer issues, and partners with global software, HPC, systems, product, and applications teams. Responsible for hiring, mentoring, succession planning, and cross-functional delivery.

Full-time summer intern supporting AI-enabled biometrics and clinical data solutions. Responsibilities include developing and evaluating machine learning, LLM, and agentic workflows; building scalable pipelines integrating clinical and statistical datasets; creating visualizations and analytical applications; automating statistical programming and clinical data lifecycle processes; and contributing to validation, documentation, traceability, and compliant deployment. The intern will collaborate with statisticians, programmers, data scientists, and clinical development teams.

Build and deploy production AI systems, including LLM agent systems, RAG applications, data pipelines, forecasting models, and decision-support tools. Responsibilities include integrating structured data and APIs, deploying scalable ML infrastructure with Docker, Kubernetes, FastAPI, and cloud platforms, monitoring model performance, and translating business problems into AI solutions. The role requires strong Python, machine learning, LLM, data engineering, and production software development skills.

Leads the design, development, deployment, and optimization of machine learning and deep learning solutions. Defines AI strategies and roadmaps, develops production-ready pipelines, implements MLOps practices, and explores Generative AI, LLM, and RAG applications. Collaborates with technical and business stakeholders, mentors AI/ML engineers, monitors model performance, and ensures privacy, security, responsible AI, and compliance.

Leads advanced identity threat research and threat hunting across hybrid and multi-cloud environments. Develops behavioral models, detection algorithms, correlation rules, and identity-focused threat intelligence for an ITDR platform. Partners with Product and Engineering, maps research to MITRE frameworks, investigates vulnerabilities, publishes technical content, pursues patents and CVEs, and mentors researchers. The role focuses on identity attacks involving Active Directory, Entra ID, Okta, PAM, and cloud IAM.