Top Machine Learning Jobs in India

Lead design and implementation of scalable, secure AI/ML architectures; develop, train, evaluate, and optimize models (NLP, CV, predictive analytics); productionize multi-agent and GenAI solutions on cloud; pilot prototypes, perform A/B testing, conduct code reviews, and collaborate with stakeholders to align solutions with business needs.

Build and maintain scalable ML pipelines and infrastructure, integrate LLMs, manage model lifecycle with MLFlow, collaborate in agile teams, write high-quality code, perform code reviews, deploy and monitor models in cloud environments.

Build, deploy, and maintain scalable ML pipelines and production models. Integrate LLMs into applications, manage model lifecycle with MLFlow, collaborate with data scientists and engineers, participate in Agile/Scrum, ensure code quality through reviews and CI workflows, and resolve infrastructure and deployment issues.

Lead and manage AI/ML projects and teams for KPMG India, delivering technology-enabled services to national and international clients while leveraging global network and local market knowledge.

Research, prototype, and integrate LLMs and NLP solutions into business use cases. Experiment with and fine-tune ML/DL models, optimize performance, build data pipelines, and collaborate with cross-functional teams to deploy LLM-based applications.

Convert data‑science prototypes into production ML services; build and operate Databricks/Spark pipelines reading/writing Snowflake; manage full model lifecycle (MLflow, CI/CD, retraining, drift monitoring); deliver/version audience segments to ad‑tech partners; ensure scalability, cost efficiency, privacy, and mentor engineers.

The Software Machine Learning Test Engineer will develop test automation for AI components, improve test infrastructure, and ensure software quality for machine learning models using Python and GitLab CI/CD.

The ML QA Lead ensures quality in Cerebras software across ML workloads, focusing on testing accuracy, performance, and usability. Responsibilities include driving software quality, automating workflows, debugging issues, and collaborating across teams.

The Senior Data Scientist will design and maintain data pipelines, optimize machine learning models, and collaborate with cross-functional teams to deliver impactful data-driven solutions.

Lead Easyship's data and AI strategy, scale analytics/data engineering/data science teams, enable self-serve analytics, ensure data quality/governance, drive ML initiatives and cross-functional data adoption.

Build and operate production ML infrastructure to convert prototype models (LLM, NLP, recommendation, forecasting, tabular) into low-latency, reliable services. Own CI/CD, containers, orchestration, inference microservices, feature pipelines, model/feature lifecycle, observability, safety/guardrails, and runtime reliability while collaborating with Applied Sciences and Product.

Design, build, and operate an LLM control plane/gateway and agent runtime for multi‑tenant automotive SaaS. Implement retrieval (graph+vector+keyword), MLOps pipelines, monitoring, safety/PII controls, autoscaling, and tooling so product teams can deploy and govern LLM and classical ML models at scale.

Create and curate AI/ML and data science course content, deliver instructor-led and virtual training, mentor candidates on project work, and stay current with emerging AI technologies. May require travel.

Design, develop, and deploy advanced ML and deep learning models for product and system improvements. Lead research on state-of-the-art techniques, perform feature engineering, model selection, training, hyperparameter tuning, and monitoring. Integrate models into production, ensure scalability and robustness, and provide technical leadership and mentorship. Collaborate with cross-functional teams and present technical strategies to senior stakeholders.

The role involves developing data-driven applications and APIs using Python, building chatbots with LLMs, and deploying scalable solutions.

Design and deliver end-to-end GenAI and ML solutions on AWS: build SageMaker pipelines, fine-tune LLMs (Llama2), implement RAG with vector stores (OpenSearch/Elasticsearch), develop LangChain-based LLM apps, perform prompt engineering, model evaluation and optimization, and collaborate with cross-functional teams to integrate models into production.

The Associate Architect will lead the development and deployment of machine learning solutions on Azure, manage the ML lifecycle, and optimize existing models while providing technical leadership to project teams.

Lead a team of ~5 ML engineers to research, prototype, and deploy multimodal ML models for robotic inspection. Curate datasets, establish evaluation methods, fine-tune generative models (esp. vision), and improve model performance for anomaly detection and object classification within industrial sites.

As a Senior Machine Learning Engineer, you'll build LLM-powered agents, collaborate on experiments, support prompt engineering, and monitor AI features in production.

Lead the development of AI subsystems in healthcare, translating requirements into technical strategies for scalable AI/ML and computer vision solutions, ensuring quality and compliance.

Lead architecture and development of agentic AI solutions for optical networks: design agent orchestration, memory, tool integration, guardrails, and human-in-the-loop workflows; convert processes into autonomous/semi-autonomous automation; document designs and best practices for scalable delivery.

Develop and optimize machine learning infrastructure and models for ad technology. Collaborate with teams for data analysis and engineering.

Design, develop, test, and deploy scalable GenAI, ML, and deep-learning solutions. Build and optimize RAG pipelines and LLM-powered, agentic AI applications. Create REST APIs and backend services, preprocess and integrate structured/unstructured data, fine-tune and monitor models, implement semantic search, and ensure production reliability, security, and performance while collaborating with cross-functional teams.

Lead and grow an ML platform team (4→10–12), owning GPU compute infrastructure, data storage and pipelines, shared tooling, architecture, cross-functional partnership, and hiring while remaining hands-on.

The role involves designing and deploying machine learning models, ensuring model performance, and optimizing ML infrastructure on GCP, collaborating closely with data engineers and scientists.