Top Machine Learning Jobs in India

Build production AI/ML systems for extracting structured data from complex documents. Responsibilities include document ingestion, OCR, confidence modeling, RAG knowledge bases, agentic workflows, human-in-the-loop review, reprocessing and diffing, model optimization, evaluation, monitoring, and governance. The role also provides technical mentorship to mid-level AI engineers and owns systems end to end.

Designs and deploys production-grade AI agents, RAG systems, multi-agent workflows, SDKs, and scalable LLM applications. Builds secure knowledge architectures, integrations, MLOps and LLMOps pipelines, governance controls, and evaluation frameworks. Serves as a technical authority in client engagements, mentors engineers, supports presales and recruitment, and drives engineering standards across AI projects.

Principal ML Engineer responsible for designing scalable software and ML systems, leading architecture decisions, developing MLOps and data pipelines, and coordinating global technical projects. The role includes code reviews, mentoring developers, managing technical debt, documenting initiatives, researching emerging technologies, and collaborating with research and product teams. Candidates need 8+ years of software development experience, ML systems leadership, software architecture expertise, cloud familiarity, Python testing knowledge, and experience with CI/CD, data transformation, model serving, or GPU-based workloads.

Analyze and manage public- and social-sector datasets across the full data lifecycle. Build ETL pipelines, AI-ready datasets, knowledge graphs, and analytical products; apply statistics, AI, and machine learning; assess data quality; and implement responsible AI practices covering fairness, explainability, governance, and risk. Collaborate with government, academia, civil society, and researchers to generate evidence, support policy and social-purpose organizations, and communicate technical findings to diverse audiences.

Leads architecture and hands-on development of scalable, secure, cloud-native enterprise applications, primarily backend services across the full stack. Owns complex initiatives from design through production, drives testing, observability, CI/CD, reliability, security, and cost optimization, and applies AI/ML and generative AI capabilities. Provides technical leadership, mentorship, architectural influence, and cross-functional collaboration.

Build and operate production machine learning and LLM systems for healthcare knowledge management and patient-facing products. Responsibilities include extracting and validating structured knowledge from unstructured sources, developing RAG and agentic pipelines, creating evaluation frameworks, and improving system accuracy, latency, reliability, and cost. The role also involves MLOps, model and data versioning, monitoring, retraining, and HIPAA-compliant handling of patient data.

Build and operate production machine learning, LLM, RAG, and agentic systems that convert unstructured healthcare information into verified structured knowledge. Develop extraction, validation, evaluation, monitoring, and orchestration pipelines; improve accuracy, latency, reliability, and cost; and maintain model, prompt, dataset, and knowledge versioning. Collaborate across product, engineering, and data teams while handling patient information under HIPAA requirements.

Leads hands-on applied AI proofs of concept from research hypothesis or business need through evaluation and technical handoff. Designs architectures and builds prototypes using LLMs, RAG, agents, machine learning, multimodal AI, enterprise data, APIs, and integrations. Develops evaluation frameworks, compares technical alternatives, documents reproducible results, identifies reusable components, and provides scale or stop recommendations. Coordinates cross-functional specialists and mentors AI engineers, POC engineers, and interns.

Leads the development and implementation of AI, machine learning, and generative AI strategies, primarily for marketing applications. Collaborates with cross-functional teams to prioritize initiatives, builds production-ready solutions, and applies advanced techniques including NLP, computer vision, deep learning, recommender systems, reinforcement learning, and large language models. Drives automated deployments through DevOps, DevSecOps, CI/CD, and full SDLC/AIDLC practices while communicating complex analytics to leadership.

Leads AI/ML teams while designing, developing, validating, and deploying production machine learning solutions. Responsibilities include Python development, end-to-end data science, LLM and RAG implementation, NLP or computer vision, API development, database management, debugging, optimization, and MLOps. The role requires collaboration across technical teams, Agile delivery, model evaluation, and adherence to security and privacy standards.

Owns Glance’s global programmatic monetization business, including revenue growth, forecasts, demand partnerships, auction strategy, yield optimization, new ad formats, AI-enabled monetization, platform compliance, industry representation, and organizational development. The role requires deep publisher-side ad tech expertise, global market experience, commercial leadership, and the ability to build and lead a high-performing team.

Lead RTL-to-GDS physical design methodology for high-performance CPU cores at advanced TSMC N3 and N2 nodes. Architect automated, ML-driven flows to optimize power, performance, and area; define hierarchical floorplanning and integration strategies; partner with EDA and IP providers; build scalable flow automation, dashboards, and execution systems; and guide early PPA modeling and timing, IR drop, electromigration, and crosstalk closure.

Build and deploy LLM-powered and agentic features for financial document and transaction processing. Develop RAG pipelines, tool integrations, structured outputs, evaluation frameworks, and production ML services. Deploy scalable systems using Docker, CI/CD, cloud platforms, monitoring, and logging. Collaborate with senior engineers, data scientists, and product managers to deliver reliable AI products.

Develops data pipelines, cleans and transforms internal and external data, and applies statistical and machine learning models to inform credit acquisition, underwriting, and line assignment strategies. The role analyzes bureau, application, alternative, and partner data to identify risks and opportunities, supports profitable decision-making, collaborates with stakeholders, and leads complex analytical assignments within Barclays’ consumer banking organization.

Leads advanced analytics, KPI governance, process mapping, and digital transformation across global procurement, supply chain, fulfillment, logistics, manufacturing, and commercial operations. Develops dashboards, predictive analyses, SAP-based reporting, and actionable insights to improve inventory, fulfillment, supplier performance, forecasting, and customer service. Partners with global stakeholders on process standardization, technology adoption, change management, and continuous improvement initiatives.

Leads engineering strategy, architecture, delivery, and operations for NVIDIA’s AI-powered employee productivity platform. Manages and develops engineering teams building web, mobile, and agentic experiences at global scale. Oversees technical roadmaps, distributed systems, enterprise integrations, reliability, security, privacy, observability, and responsible AI practices while partnering with Product, Design, HR, Security, SRE, and business stakeholders.

Provides technical leadership for Netskope’s data security engineering team. Designs and develops scalable, high-performance dataplane services and content inspection solutions using C++, C, Golang, Python, advanced algorithms, machine learning, and AI-assisted tools. Leads complex technical initiatives, mentors engineers, troubleshoots software, and delivers cloud-deployed security solutions that prevent sensitive data loss.

Leads AI opportunity discovery and rapidly builds validated prototypes using traditional machine learning, RAG, agentic workflows, LLMs, or rule-based systems. Partners with business stakeholders to define problems, evaluate accuracy, cost, and latency, and make evidence-based build decisions. Documents validated POCs for handoff to AI platform engineering. The role requires Python, SQL, LLM APIs, prompt engineering, vector databases, orchestration frameworks, evaluation tools, cloud platforms, and strong stakeholder communication.

Designs, develops, evaluates, deploys, and maintains machine learning, deep learning, generative AI, and LLM-powered systems. Builds data pipelines, MLOps processes, RAG applications, agentic workflows, evaluation frameworks, guardrails, and structured output validation. Collaborates with data scientists, software engineers, and product managers to deliver reliable AI products and stays current with emerging AI technologies.

Leads the design, training, optimization, and production deployment of advanced AI/ML systems, including LLMs, GraphRAG pipelines, embeddings, semantic retrieval, NLP, reinforcement learning, and distributed multi-GPU training. Translates research into scalable clinical AI solutions, collaborates with cross-functional teams, and advances methods for information extraction, document understanding, and summarization. Requires deep machine learning expertise, production experience, strong programming skills, and a substantial publication record.

Lead Tide’s engineering organization for fraud, financial crime, and ongoing monitoring platforms. Set strategy across backend, frontend, data science, ML, and analytics engineering; own decisioning platforms and model lifecycles; scale agentic AI in regulated workflows; and align risk, compliance, operations, payments, and technology teams. The role is accountable for improving fraud detection, reducing losses and monitoring costs, increasing automation, and building globally scalable platforms that meet regulatory standards.

Leads enterprise data management and quantitative analytics across Risk and Compliance. Develops AI/ML solutions, data pipelines, dashboards, metadata and governance programs, data quality controls, calculation services, and executive reporting. Partners with business and engineering teams to deliver scalable, audit-ready platforms, improve risk insights, and align data strategy with organizational goals.

Lead the design, automation, deployment, and support of a secure, scalable AWS-based machine learning platform. Build infrastructure with Terraform, containerized workloads with Docker and Kubernetes, and automated delivery pipelines using GitLab CI/CD and related tools. Develop Python automation, support AWS migrations, implement monitoring, logging, security, and disaster recovery, and promote DevOps and infrastructure standardization across engineering teams.

Build LLM-powered code-generation, document-understanding, context-synthesis, and agentic workflows. Develop evaluation frameworks for query processing, collaborate with customers and founders on product and engineering direction, and help shape the company’s engineering culture and technology stack. The role requires strong analytical skills and experience solving difficult NLP problems, with search, retrieval, LLM, and evaluation pipeline experience preferred.

Develop and maintain Databricks-based ETL/ELT pipelines and build RAG applications using Python, LLMs, embeddings, vector databases, and prompt engineering. Prepare and validate data, integrate AI models with enterprise platforms, optimize solutions for scalability, and collaborate with data engineering and business teams. Preferred experience includes Spark, cloud platforms, LangChain, LlamaIndex, MLflow, REST APIs, and cloud AI services.