Top Machine Learning Jobs in India

Lead enterprise AI and full-stack platform architecture across company initiatives. Design and ship microservices, REST APIs, RAG and multimodal LLM features, agentic workflows, and reusable AI components. Make end-to-end technical decisions balancing reliability, performance, and cloud costs. Mentor engineers and guide contractors and partners while establishing development, code review, and quality standards for globally distributed teams.

Build and optimize multilingual ASR/TTS and voice-to-voice systems for conversational AI agents. Develop agentic RAG pipelines, LLM orchestration, self-learning agents, and guardrails supporting interruptions and natural pauses. Deploy scalable, secure, reliable ML systems in low-latency real-time environments while collaborating with product, engineering, and MLOps teams.

Translates business challenges into data science problems, develops and validates statistical models and OKRs, wrangles complex datasets, and delivers insights through data storytelling. Leads modeling decisions, feature engineering, machine learning evaluation, workflow optimization, and scalable solution implementation. Collaborates with engineering and product teams, promotes data literacy, and applies cloud, big data, MLOps, and potentially deep learning and agentic AI technologies.

Lead enterprise AI architecture and AI-assisted software delivery across GE Vernova Grid Software Services. Define reusable AI reference architectures, design standards, MLOps and LLMOps requirements, approved tooling, and governance practices. Review complex AI solutions for scalability, security, maintainability, and reuse; partner with Digital and IT; guide platform integration; mentor AI architects; and support high-risk initiatives, vendor evaluations, and model governance.

Design and implement AI, machine learning, and computer vision solutions, including REST APIs, vector database schemas, scalable queries, proof-of-concepts, automated testing, and Azure DevOps deployment pipelines. Collaborate with data scientists, data engineers, and DevOps teams to build secure, scalable cloud applications. Troubleshoot issues, maintain technical documentation, improve coding and DevOps standards, and mentor interns and junior engineers.

Lead design, development, and deployment of AI/ML solutions to improve health outcomes. Collaborate cross-functionally, follow company policies and employment terms, and adapt to changing assignments, teams, locations, or work arrangements as needed.

Architect and productionize ML/GenAI solutions on Databricks. Design RAG and agentic systems, build end-to-end MLOps pipelines, optimize training/inference, integrate cloud services, and support solution architects in technical presales and customer deployments.

Design and architect production-grade ML and GenAI solutions on Databricks, including RAG and agentic systems, MLOps pipelines, training/inference optimization, model monitoring, and customer-facing technical enablement and mentorship.

Lead the ethical use and governance of AI, ensuring compliance with legal standards, managing AI risks, and fostering collaboration across teams.

Drive advanced brand and pipeline analytics for commercial and medical stakeholders: perform segmentation, adherence, HCP network and patient-flow analyses; automate reporting and models; manage analytics projects and vendors; deliver actionable insights to inform brand strategy, investment decisions, and cross-functional Value Team objectives while maintaining compliance and agile practices.

Lead product strategy and execution for Paytm Ads to build a performance-first advertising stack. Drive audience systems, privacy-safe data activation, ML-led bidding and ranking, real-time decisioning platforms, attribution and conversion tracking, DSP integrations and monetization. Own 0→1 product journeys, scale solutions, and collaborate cross-functionally with engineering, data science, sales and GTM teams to improve advertiser ROAS, CAC and conversions.

Lead fraud detection, investigations, and compliance for digital payments (UPI, IMPS, wallets, merchant platforms). Build and deploy risk rules using Python and SQL, optimize ML models, oversee real-time monitoring and investigations, author SOPs, and liaise with RBI, NPCI, FIU and law enforcement while managing large operational teams.

Build and deploy ML models on structured and unstructured data, design A/B tests, create visualizations, collaborate cross-functionally, operationalise pipelines with data engineering, develop domain expertise, and translate analyses into stakeholder recommendations.

Lead end-to-end procurement analytics: extract and harmonize ERP spend data, build SQL/Python pipelines and advanced Excel decision models, identify savings levers, develop should-cost and predictive ML models, apply generative AI and NLP for contract and RFP analysis, and independently deliver client-ready insights and automation.

Lead and govern the AI function from India: set strategy and 12–24 month roadmap, run portfolio governance and responsible-AI guardrails, align with executives, hire and lead delivery teams, manage budget/cloud/vendor relationships, prioritize use cases, remove cross-functional blockers, and drive enterprise-scale GenAI/ML production and value realization.

Lead design and delivery of scalable AI-driven automation and BI solutions for KYC operations. Build advanced Tableau dashboards, integrate ML/AI workflows, ensure governance and validation, and mentor technical teams while collaborating cross-functionally.

Lead architecture and delivery of scalable automation and AI-driven workflows for KYC operations. Build advanced Tableau dashboards, integrate ML models, ensure output validation and governance, collaborate with data engineers and stakeholders, and mentor technical teams while enforcing compliance and performance standards.

Design and build scalable ETL/ELT pipelines in Python and PySpark, integrate Generative AI/ML into data workflows, enforce data quality, automate CI/CD, optimize performance, mentor peers, and collaborate with product and engineering teams to deliver a production-grade data platform.

Lead data science efforts for InMobi DSP: ideate solutions, design and deploy ML models at scale, run experiments, analyze results, collaborate with product and engineering, and drive measurable business impact.

Develop and deploy production-ready AI and machine learning applications using Python, cloud AI services, and on-premises pipelines. Apply generative AI, deep learning, neural networks, chatbot development, and image processing. Work independently as a subject matter expert, troubleshoot technical challenges, collaborate with teams, contribute solutions, and guide junior team members.

Design, build, and scale MLOps/LLMOps pipelines and secure automated deployments on Azure. Ensure governance, observability, and evaluation of production models. Partner with data scientists and product teams to operationalize AI, implement safe rollouts (A/B tests, canaries, human-in-the-loop), and maintain model performance and compliance.

Build and manage the full lifecycle for self-hosted LLMs, including training data pipelines, SFT and DPO fine-tuning, evaluation, benchmarking, quantization, deployment, monitoring, and continuous improvement. Deploy models on AWS GPU infrastructure and SageMaker, automate ML CI/CD, and manage Docker and Kubernetes workloads on EKS. Implement A/B, canary, and shadow deployments with automated promotion and rollback based on performance and operational metrics. Collaborate with data science, ML engineering, and DevOps teams.

Own production machine learning environments and MLOps infrastructure. Design and maintain pipelines for data ingestion, feature engineering, model training, deployment, and monitoring. Productionize ML and GenAI services, manage feature stores and search infrastructure, automate CI/CD, implement observability, optimize costs, handle security patching, and participate in 24/7 on-call rotations. Collaborate with data science, development, QA, and operations teams to improve deployment reliability and governance.

Design, build, and deploy production-grade Generative AI systems (LLM-based, RAG, agents) for terminal operations. Develop data pipelines, evaluation frameworks, monitoring, and guardrails. Collaborate with stakeholders to translate operational challenges into measurable AI solutions, ensuring production readiness, performance optimization, and continuous improvement.

Architects and delivers production-grade machine learning, deep learning, generative AI, agentic AI, and LLM systems. Owns the full AI lifecycle from problem framing and data preparation through deployment, monitoring, evaluation, and optimization. Builds scalable data, feature, MLOps, and LLMOps pipelines; develops APIs and enterprise integrations; applies responsible AI, security, privacy, and governance practices; and influences architecture, engineering standards, product roadmaps, and technical capabilities through leadership, mentoring, and cross-functional collaboration.