Top Machine Learning Jobs in India

Lead post-deployment operations and optimization of Agentic AI, LLM integrations, and Python-based analytical pipelines. Supervise a data science operations team, ensure secure high-availability production systems, manage integrations with enterprise systems (SAP, Salesforce, Postgres), and drive continuous improvement, monitoring, and adoption of analytics solutions.

Design, develop, evaluate, and deploy AI/ML models and scalable solutions for business applications. Responsibilities include data preprocessing, feature engineering, model training, visualization, API development, production integration, data security, documentation, and continuous research into emerging AI/ML technologies. The role collaborates with software engineers, DevOps teams, product managers, and domain experts.

Design, build, and operate production LLM applications for personalized multilingual learning. Responsibilities include RAG and agentic pipelines, tool-calling systems, backend services, data pipelines, evaluation frameworks, monitoring, cost and latency optimization, model assessment, and open-source or sovereign LLM integration. The role also involves architecture ownership, production deployment, and potentially fine-tuning foundation models using methods such as LoRA, QLoRA, and SFT.

Architect and improve autonomous AI optimization algorithms for a marketing platform. Analyze A/B tests, integrate AI models, develop multi-touch attribution, expand advertising-channel capabilities, reduce inference costs and latency, incorporate customer feedback, and maintain reliable, continuously improving optimization loops.

Design and develop machine learning models analyzing powertrain and vehicle dynamics data for vehicle health, performance, fuel pilferage, and driver behavior use cases. Research statistical, mathematical, automotive, and physics-informed machine learning algorithms. Implement models in software, collaborate with data engineering and GUI teams for deployment and performance, and support service teams with technical guidance. The role also involves mentoring ML engineers and analysts and validating new model designs.

Build and deploy classical machine learning and generative AI solutions, including regression, classification, clustering, LLM, RAG, and prompt-engineering applications. Develop real-time APIs, integrate vector databases, optimize inference, monitor production models, and promote safe, explainable, unbiased AI outputs. Collaborate with business teams to translate requirements into ML use cases. Required expertise includes Python, common data science libraries, LLM frameworks, transformer architecture, NLP, REST APIs, cloud platforms, Docker, and Kubernetes.

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.

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.

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.

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 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.

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.

Work across full software development lifecycle: design, code, integrate, deploy, test and document applications. Contribute to mobile (iOS/Android) projects and a machine-learning project using semantic search, ontologies and Bayesian methods. Support QA, evaluate new technologies, deploy CI/WordPress related solutions, and help improve processes (Lean).

Develop end-to-end software prototypes and research new technologies (e.g., blockchain), apply CS/math skills, work with distributed computing and polyglot codebases, explore ML/NLP/cryptography and game-theory related problems, and deliver quick prototypes.

Six-month NLP research internship focused on developing and applying machine learning and deep learning algorithms for natural language understanding in healthcare. Responsibilities include research, prototyping, experimentation, performance analysis, and presenting results.

Design, develop, and test diagnostic machine learning models (supervised and unsupervised) to improve output monitoring; build business cases to scale prototypes to production; collaborate with research, IT, and content experts in a multinational team to enhance product quality.

Design, develop, and implement advanced statistical and machine learning algorithms. Analyze disparate data sources to discover insights, build predictive models, prototype solutions, and collaborate with engineers to productionize models. Communicate findings to business and IT stakeholders and define system and functional requirements.

The Senior Analyst will independently deliver analytical work in procurement, using SQL, Python, and AI tools to improve business insights and decision-making.

Lead and manage a team of Software Engineers focusing on building tooling for internal teams at Nike, emphasizing collaboration and modern software practices.

Lead automation and analytics for internal audit: design Power BI dashboards, data analytics, and AI/RPA pilots to enhance SOX testing, control validation, anomaly detection, and continuous monitoring. Liaise with IT, business, and external auditors; coach team members; support remediation and audit transformation.