Top Machine Learning Jobs in India

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.

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.

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.

Lead the architecture and delivery of production AI and machine learning platforms, including representation learning, embeddings, vector search, retrieval-augmented generation, foundation models, agentic AI, and multimodal systems. Set technical direction, evaluate emerging technologies, define measurable success criteria, mentor engineers and data scientists, and collaborate with research, engineering, and product teams on scalable, explainable, responsible solutions for spatial and operational challenges.

Leads the architecture, development, and production delivery of AI/ML capabilities in enterprise healthcare applications. Builds ML and generative AI solutions, RAG systems, model-serving APIs, MLOps workflows, distributed cloud services, and CI/CD pipelines. Ensures responsible AI, security, privacy, reliability, monitoring, and operational readiness. Influences architecture across teams, mentors engineers, documents technical decisions, and delivers reusable production components.

Leads planning and execution of complex data science projects from business understanding through model deployment and monitoring. Coordinates product, engineering, data science, and leadership teams; manages roadmaps, dependencies, risks, trade-offs, escalations, and status reporting. Translates technical concepts into business language, drives continuous improvement, facilitates retrospectives, and ensures alignment across matrixed teams.

Designs and implements secure AI solutions and technical security controls; assesses AI systems for security and privacy risks; develops automation, monitoring, and detection capabilities; translates policies and regulations into controls; supports governance and audit readiness; and partners with engineering, security, risk, and business teams. The role also provides technical leadership and AI security education in an onsite, overnight-shift environment.

Develops hardware-accelerated AI software for multimedia platforms, including audio, video, augmented reality, computer vision, and streaming applications. Responsibilities include designing algorithms, deploying applications across platforms, profiling and optimizing AI pipelines, addressing system-level resource challenges, and writing Python and C++ code. Requires substantial Linux software development experience, cloud-native architecture knowledge, and expertise in computer vision, machine learning, and deep learning.

Design and scale observability and reliability frameworks using metrics, logs, traces, predictive analytics, anomaly detection, and machine learning. Establish dashboards, SLAs, SLOs, SLIs, and high-availability architecture for distributed systems. Develop AIOps capabilities for root-cause analysis, predictive capacity planning, alert deduplication, and noise reduction. Mentor engineering teams and communicate complex technical concepts to leadership.

Develop and deploy production-ready AI and machine learning applications using cloud AI services, generative AI, deep learning, neural networks, chatbots, and image processing. Lead model development, coordinate cross-functional teams, make technical decisions, mentor junior engineers, and ensure scalable, reliable solutions aligned with organizational objectives.

Leads global Quality, Operations, and Continuous Improvement platforms, analytics, AI transformation, governance, and operational excellence. Owns product roadmaps, Agile delivery, platform adoption, business cases, ROI, stakeholder alignment, and enterprise-scale improvement initiatives across manufacturing and business units.

Develop and maintain reusable platform services, APIs, and backend components using Java, .NET, or Python. Build cloud-native applications on AWS, implement automated testing and CI/CD practices, and collaborate with architects and engineers to deliver scalable, resilient microservices. Front-end development with React or Angular is desirable, along with experience in document processing, OCR, NLP, machine learning, Docker, Kubernetes, or AWS certifications.

Owns account management, revenue targets, client relationships, and media growth for key advertising accounts in India. Drives cross-selling, upselling, campaign optimization, issue resolution, and strategic client conversations. Coordinates Sales, Product, Ad Operations, Engineering, Finance, Legal, Marketing, and other teams while providing data-driven insights and feedback on product and market needs.