Top Machine Learning Jobs in India

Designs and deploys scalable machine learning solutions using Python, TensorFlow, Kubeflow, Docker, and Kubernetes. Develops LLM applications and RAG architectures, integrates OpenAI and other AI services into production, manages cloud-native orchestration workflows, and monitors model and infrastructure performance to ensure reliability and continuous improvement.

Lead design and implementation of ML tools for scenario classification, anomaly detection, sensor data quality, behavior analytics, and automated reporting. Build scalable pipelines and automated data curation for camera, radar, and LiDAR; create SIL/HIL replay interfaces, ground-truthing and annotation tools, and offline benchmarking. Integrate ML modules into simulation, HIL/SIL, and CI/CD ecosystems, implement reproducible workflows (Docker, CI/CD, artifact versioning, automated tests), monitor model/data quality and mentor junior engineers while collaborating with verification and perception teams.

Leads data science initiatives by translating business problems into projects, preparing and analyzing data, engineering features, developing and validating machine learning models, and monitoring performance. Uses Python, SQL, Databricks, and big data platforms. Communicates insights to technical and non-technical stakeholders, documents processes, promotes best practices, and mentors junior data scientists while supporting team development and collaboration.

Designs, builds, tests, and deploys advanced machine learning and NLP systems for complex business challenges. Researches zero-shot and few-shot learning, embeddings, fine-tuning, transformers, and LLM applications. Develops scalable, secure, high-throughput architectures and reusable modeling tools using Python and modern ML infrastructure. Collaborates with data scientists, manages stakeholder communications, handles shifting priorities, and promotes high-quality production code and knowledge sharing.

Build backend and platform components for an AI agent layer, including guardrails, policy enforcement, semantic caching, MCP-based tool governance, access controls, and application integrations. Develop scalable APIs and services, integrate across four applications, write comprehensive tests, troubleshoot production and performance issues, and collaborate with architecture and application teams on secure, reliable integrations.

Design, operate, and evolve ResMed’s AI/ML platform on AWS and Kubernetes. Provision infrastructure with Terraform, own CI/CD for models and applications, and build observability with Prometheus, Loki, Grafana, or Datadog. Partner with data science and GenAI teams on model serving, agentic workloads, autoscaling, secure tool integration, and cost and latency controls. The role also automates platform workflows, supports LLMOps and RAG infrastructure, runs proofs of concept, and contributes to code reviews and mentoring.

Leads the design and development of generative and agentic AI applications using LLMs, NLP, deep learning, retrieval, RAG, and orchestration frameworks. Builds agent architectures, evaluation frameworks, data-processing pipelines, guardrails, and responsible AI controls. Optimizes models through prompting, reranking, embeddings, model selection, and fine-tuning. Translates business needs into measurable AI solutions, stays current with emerging technologies, leads technical discussions, and mentors team members.

Design and deliver production-grade AI/ML architectures for education and assessment platforms. Translate stakeholder requirements into secure, cost-aware blueprints covering RAG, multi-agent systems, LLM judging, OCR, human review, cloud infrastructure, LLMOps/MLOps, observability, governance, and evaluation. Lead workshops, architecture decisions, production readiness, FinOps, sovereign hosting, and technical mentorship across international government and education programs.

Build and deploy enterprise AI/ML and Generative AI applications across the full lifecycle, including data pipelines, RAG systems, agentic workflows, APIs, evaluation, monitoring, and cloud deployment. Develop production Python services, CI/CD workflows, containerized applications, and secure AI solutions. Collaborate with product, engineering, cloud, data, and business teams while documenting architectures and participating in agile delivery.

Leads the design, development, deployment, and operation of production AI, GenAI, RAG, and agentic solutions. Responsibilities include building data and model pipelines, MCP integrations, evaluation and observability systems, secure cloud-native services, and enterprise connectors. The role requires hands-on Python engineering, AWS or Azure deployment, MLOps/LLMOps practices, architecture leadership, stakeholder collaboration, and mentoring. Production MCP experience and a current AWS AI/ML or Azure AI certification are mandatory.

Lead the design, development, deployment, and operation of machine learning, generative AI, RAG, and agentic AI solutions. Responsibilities include model optimization, AI architecture, MLOps and GenAIOps, monitoring, governance, security, responsible AI, production troubleshooting, and cross-functional delivery of scalable enterprise AI services.

Build and productionize agentic AI and LLM features for an enterprise SaaS platform focused on data privacy and AI governance. Responsibilities include developing LangChain and LangGraph workflows, RAG pipelines, AI asset discovery, safety guardrails, model evaluations, backend API integrations, and observability. The role requires Python, cloud AI platforms across AWS and Azure, vector databases, prompt engineering, and experience deploying ML or GenAI applications in production.

Build and maintain production-grade machine learning pipelines and APIs for industrial data contextualization. Responsibilities include deploying research prototypes, writing testable Python, containerizing applications with Docker, developing RESTful and gRPC services, fine-tuning NLP and vision-language models, processing large datasets, monitoring model performance, and collaborating with engineering, data science, and product teams.

Designs, develops, and optimizes enterprise data platforms, ETL/ELT pipelines, data warehouses, integration frameworks, and analytics solutions. Leads architecture, technical design, performance tuning, DevOps automation, troubleshooting, and production readiness. Partners with product, architecture, analytics, AI/ML, and business teams to deliver governed, scalable data foundations. Mentors engineers, conducts technical and code reviews, and supports modernization using Talend, Snowflake, Databricks, Python, SQL, and cloud technologies.

Design, develop, and deploy ML models on Google Cloud (Vertex AI). Process and feature-engineer large datasets in BigQuery, integrate ML pipelines with GCP services, collaborate with stakeholders, and produce analytical reports and visualizations to support data-driven decisions.

Design, develop, train, evaluate, and deploy machine learning models and algorithms. Prepare and transform large datasets, perform feature engineering, tune hyperparameters, and assess model performance. Collaborate with data scientists, software engineers, and business stakeholders to translate requirements into AI/ML solutions. Present findings, participate in design reviews, provide peer feedback, and maintain relevant dashboards, databases, and business intelligence tools.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Researches, prototypes, evaluates, and productionizes AI/ML solutions for engineering and industrial use cases. Builds agent-accessible artifacts, reusable software components, benchmarking approaches, and production-ready capabilities. Collaborates with data engineers, software engineers, cloud architects, and engineering stakeholders. Uses GCP, GitHub workflows, CI/CD, and automated testing to deploy and validate solutions, while communicating results through documentation, publications, patents, or open-source contributions.

Develop and deploy AI/ML solutions for audience measurement using statistical modeling, classical machine learning, deep learning, and multimodal LLMs. Responsibilities include data preprocessing, feature engineering, exploratory analysis, model evaluation and optimization, production deployment, MLOps collaboration, documentation, stakeholder communication, research, and mentoring junior data scientists.

Design, develop, and maintain compiler front-end, middle-end, and back-end components. Implement language features, code generation, optimization algorithms, and customer-specific compiler solutions. Collaborate with customers and cross-functional teams to translate requirements into technical specifications, stay current with compiler technologies, and mentor junior engineers.

Build full-stack infrastructure, DevOps, and MLOps tools for AI development teams. Responsibilities include CI/CD, reproducible environments, experiment and evaluation workflows, self-service applications, observability, cost tracking, deployment automation, identity and access services, LLM gateways, and usage metering. Collaborate directly with engineers and researchers to improve internal workflows, reliability, and operational efficiency.

Designs, develops, deploys, and scales predictive machine learning, deep learning, generative AI, and agentic AI solutions. Builds LLM, RAG, and cloud-based AI systems, performs research, develops ETL pipelines using SQL, and translates business requirements into production solutions. Leads junior AI engineers and communicates technical concepts to technical and non-technical stakeholders.

Leads validation strategy and automated testing for NAND-based SSD firmware across functional, performance, reliability, interoperability, and customer qualification domains. Develops test plans and automation frameworks, debugs NVMe and SAS issues, analyzes quality risks, and drives issue closure with cross-functional engineering teams and hyperscale customers. Applies AI and machine learning to test generation, failure analysis, anomaly detection, reporting, and predictive quality. Mentors engineers and leads complex validation programs.

Designs and develops enterprise software and cloud services in C++, focusing on Windows/Linux internals, high-performance scalable systems, virtual desktops, and AI/ML integration. Leads architectural initiatives, participates across the software development lifecycle, improves DevOps and CI/CD processes, troubleshoots complex production issues, supports customer escalations, and mentors junior engineers. The role is hybrid in Chennai or Bangalore with approximately 10% domestic or international travel.

Design, develop, train, evaluate, and scale machine learning models (including generative AI). Extract business entities and insights from documents, build/maintain RESTful and GraphQL APIs, deploy via Docker and Azure, and follow DevOps/CI-CD practices while documenting work and collaborating with stakeholders.