Top Machine Learning Jobs in India

Designs and develops traditional machine learning, deep learning, generative AI, NLP, LLM, RAG, and agent-based solutions. Responsibilities include building AI applications, optimizing NLP use cases, evaluating and fine-tuning foundation models, implementing responsible AI practices, and ensuring scalability, security, explainability, and reliability. The role requires extensive AI/ML engineering experience, cloud and DevOps expertise, proficiency with major ML frameworks, and strong Python or related programming skills.

Develop and productionize advanced AI/ML solutions, including LLM training and fine-tuning, GraphRAG, embeddings, semantic retrieval, distributed multi-GPU training, and reinforcement learning alignment. Build hybrid NLP systems, optimize model architectures and retrieval pipelines, and collaborate cross-functionally to deploy AI solutions. The role requires deep expertise in machine learning, deep learning, NLP, transformer and state-space architectures, cloud ML platforms, data preparation, and production-grade model development.

Lead a healthcare analytics and clinical configuration team, owning end-to-end auto-decisioning strategies for new client implementations. Translate business needs into scalable analytical frameworks, develop performance tracking and optimization initiatives, and deliver actionable insights from healthcare data. Partner with data governance and cross-functional teams to ensure explainable, responsible analytics. Manage team hiring, mentoring, coaching, and day-to-day operations while influencing product and business outcomes.

Performs global trade compliance screening for HPE transactions, including export controls, embargoes, restricted-party lists, and licensing decisions. The role analyzes performance metrics, backlogs, demand, and staffing needs; supports quality control, training, process improvement, automation, reporting, and stakeholder communication. It requires rotational AMS shifts, weekend coverage, and hybrid office collaboration.

Develop, test, deploy, and maintain production AI/ML models and systems for consumer-facing healthcare platforms. Build LLM applications, contribute across the AI lifecycle, manage cloud infrastructure using Infrastructure as Code, and improve reusable ML platforms. Collaborate with product and engineering teams, troubleshoot production issues, evaluate emerging technologies, and deliver scalable solutions involving NLP, computer vision, personalization, recommendation systems, and anomaly detection.

Owns enterprise KPI reporting, executive dashboards, finance and FP&A reporting automation, data governance, and scalable analytics products. Partners with global leaders and technical teams to translate business needs into data-driven and AI-enabled solutions. Leads AI integration, roadmap planning, stakeholder alignment, cross-functional projects, commercialization, and team mentoring while using Tableau, Power BI, SQL, and Python.

Owns a cybersecurity detection coverage area, including backlog prioritization, technical direction, detector design, implementation, code reviews, tuning, and performance measurement. The role improves detection fidelity, ATT&CK coverage, false-positive reduction, and regression prevention while translating threat intelligence, threat hunting, and SOC feedback into roadmap priorities. It requires deep expertise in at least one detection domain and hands-on detector-as-code development with version control and controlled deployment.

Build, train, evaluate, and deploy machine learning and LLM-based safety models, guardrails, and content moderation systems. Develop evaluation frameworks, adversarial testing, and low-latency inference solutions for high-throughput production workloads. Research defenses against jailbreaks, prompt injection, data leakage, and harmful content. Support AI governance, compliance, audit logging, and watermarking. Collaborate across engineering, product, legal, and trust teams while mentoring engineers.

Lead enterprise-scale AI/ML and mathematical optimization initiatives from business problem definition through production deployment. Design hybrid machine learning and optimization solutions using frameworks such as Pyomo, OR-Tools, Gurobi, CPLEX, PyTorch, and TensorFlow. Develop proof-of-concepts, decision systems, MLOps workflows, and production-ready architectures while collaborating with engineering, product, business, and leadership teams. Preferred expertise includes quantum optimization, reinforcement learning, generative AI, healthcare analytics, data platforms, and responsible AI.

Develop and optimize machine learning, generative AI, embedding, and RAG solutions. Build enterprise AI applications using vector databases, semantic search, LLMs, and LangChain. Analyze large datasets, integrate scalable AI pipelines with enterprise systems, improve model performance, and collaborate with data, product, and platform teams. Mentor engineers and interns while communicating technical concepts to stakeholders.

Build and deploy agentic AI workflows, RAG pipelines, and cloud-native services. Integrate enterprise systems through APIs, MCP servers, message buses, and event-driven architectures. Apply responsible AI controls, implement LLM evaluation frameworks, optimize cost and latency, and support observability and CI/CD. Mentor junior developers while contributing to secure, scalable AI solutions.

Leads enterprise AI/ML and generative AI delivery, cloud modernization, responsible AI practices, platform reliability, and engineering team development. Oversees technical roadmaps, scalable architecture, automation, monitoring, governance, risk mitigation, and operational excellence. Partners with business, product, architecture, and engineering stakeholders to deliver secure, compliant, measurable AI solutions while mentoring distributed teams and managing priorities, resources, dependencies, and organizational impact.

Design and develop autonomous agentic AI and multi-agent systems for MarTech use cases. Build LLM-powered applications, orchestration workflows, reusable tools, enterprise integrations, and human-in-the-loop governance. Implement enterprise-scale RAG pipelines involving document ingestion, embeddings, retrieval, ranking, and vector databases. Evaluate and optimize models for quality, latency, cost, and performance while developing monitoring and evaluation frameworks. Full-stack development includes Java, React, Next.js, Python, REST APIs, SQL, and Azure cloud services.

Build and deploy production-grade AI solutions for banking client onboarding, KYC, and document operations. Develop document intelligence models, LLM-based extraction and summarization frameworks, automation pipelines, APIs, and reusable AI modules. Collaborate with stakeholders, monitor and retrain models, analyze data, ensure privacy and governance compliance, and explore emerging AI technologies. The role may eventually expand into leadership of a specialized AI team.

Build production generative AI systems for enterprise payments, including search, conversational assistants, retrieval pipelines, and agentic workflow automation. Develop and support reliable, scalable services using Python, APIs, microservices, and AWS. Implement tool orchestration, error recovery, human-in-the-loop patterns, and LLM evaluation frameworks while addressing latency, observability, cost, and governance requirements. Collaborate with engineering, product, and business stakeholders through design, implementation, testing, and production support.

Sell SailPoint's IGA identity security SaaS to $5B+ enterprises by engaging C-level stakeholders, managing long sales cycles with POCs/RFPs, collaborating with GSIs and channel partners, developing account and opportunity plans, and meeting quota using Challenger methodology.

Design, develop, train, evaluate, deploy, and maintain machine learning and deep learning models. Build data pipelines, MLOps processes, and production-grade generative AI applications using LLM workflows, RAG, agent orchestration, prompt engineering, and structured output validation. Collaborate with technical and product teams to integrate AI solutions, establish reliability and evaluation frameworks, and apply emerging research to business problems.

Develop and govern high-quality data attributes for credit card machine learning models across the customer lifecycle. The role designs attribute libraries, develops predictive modeling solutions, monitors data quality, evaluates data sources, identifies risks and revenue opportunities, and delivers models into production. It requires advanced statistical and machine learning expertise, large-scale data analysis, coding proficiency, stakeholder collaboration, and communication with senior management.

Build and deploy agentic AI workflows, RAG pipelines, cloud-native services, and enterprise integrations. Develop safe tool-calling and multi-agent systems, implement responsible AI controls, establish LLM evaluation frameworks, optimize cost and latency, and mentor junior developers. The role requires production engineering across cloud platforms, containers, CI/CD, observability, APIs, event-driven architectures, and vector databases.

Lead the architecture, design, and delivery of enterprise-scale AI-powered, cloud-native solutions. Drive adoption of generative and agentic AI, AI/ML platforms, automation, Azure architecture, Kubernetes, DevSecOps, and platform engineering. Establish technical standards and best practices, lead cross-functional initiatives, mentor engineers, improve reliability and observability, and influence architecture decisions across teams while aligning technology solutions with business strategy.

Design, build, and optimize LLM and Generative AI solutions including custom embeddings, RAG pipelines, vector DBs, and enterprise integrations. Experiment with models, analyze large datasets, scale AI pipelines, partner with cross-functional teams, and mentor engineers.

Designs, develops, and deploys AI-enabled, cloud-based, full-stack enterprise applications for smart manufacturing. Integrates generative AI, machine learning, and automation into scalable business solutions; builds Angular frontends and .NET APIs and microservices; implements Azure cloud and DevOps practices; optimizes application performance and security; contributes to architecture and modernization initiatives; and mentors engineers while collaborating with stakeholders and technical teams.

Build and lead production AI/ML components, including embeddings, retrieval, agents, and multimodal systems, for reuse across domain teams. Translate architectural direction into technical specifications and production-ready implementations, make implementation-level decisions, identify scalability issues, and mentor engineers. The role requires deep expertise in representation learning, vector search, retrieval-augmented systems, deep learning, LLMs, agent frameworks, distributed systems, and ML engineering.

Lead the architecture, development, deployment, and monitoring of production-grade machine learning systems for high-definition, ADAS, ISA, and IVI maps. Lead work across computer vision, NLP, deep learning, Generative AI, Agentic AI, MLOps, data ingestion, model training, inference, and scalable cloud or in-vehicle deployment. Provide technical leadership, conduct architecture and code reviews, integrate ML outputs with mapping workflows, and translate business requirements into reliable ML system designs.

Design, develop, and deploy production-grade AI systems using generative AI, LLMs, agentic architectures, retrieval, deep learning, and multimodal models. Build scalable representation and vector-search layers, lead experiments, optimize and evaluate models, and apply responsible AI practices. Collaborate cross-functionally to solve spatial and business challenges, mentor engineers and data scientists, and contribute to patents and publications. The role requires strong Python and ML framework expertise, production MLOps experience, and independent architectural decision-making.