Top Machine Learning Jobs in India

Design, implement, deploy, and maintain large-scale recommendation and ML systems. Perform data analysis, experimentation (A/B testing), collaborate with product teams, monitor production systems, and apply MLOps practices using cloud and Kubernetes.

Architect, develop, and enhance AI/NLP systems and platform components. Design and customize machine learning and deep-learning models, implement NLP pipelines, work with Spark and common ML frameworks, deploy on AWS/Linux, and collaborate using Agile, Git/SVN, and Jira.

Work on large-scale machine learning projects for personalization, fraud detection, and NLP using big data systems. Research, design, and implement algorithms for classification, regression, and collaborative filtering, and communicate results to stakeholders.

Design, develop, and deploy production-grade ML solutions and Digital Twin models for smart manufacturing. Build predictive/prescriptive models (predictive maintenance, anomaly detection, throughput, scheduling), create feature and streaming pipelines, implement MLOps (training/deployment, model registry, drift monitoring), collaborate with engineers and SMEs, support plant visits, and ensure scalable, monitored AI solutions for industrial operations.

Develop and deploy LLM-based AI solutions and RAG applications using JavaScript/Node.js. Build AI agents, REST APIs, vector search integrations, and cloud-native microservices. Collaborate on prompt engineering, use cloud and container technologies, and support enterprise-scale AI deployments.

Seeking a Machine Learning Engineer to work on workforce development and artificial product development, applying machine learning and deep learning concepts.

Design and implement agentic architectures for humanoid robots, collaborating with teams to enhance performance, safety, and reliability through advanced methodologies.

Design, build, and deploy ML models for fraud, risk, and intelligence products; translate business problems into data solutions; develop anomaly detection and graph-based methods; own end-to-end model development, deployment, and monitoring; run experiments and manage large multi-country datasets; partner cross-functionally to ship privacy-safe data products and explain model outputs to technical and non-technical stakeholders.

Design, build, and deploy scalable AI/ML solutions including predictive models, recommendation systems, forecasting, anomaly detection, and Generative AI applications (LLMs, RAG). Implement MLOps (CI/CD, monitoring, versioning), production model deployment, data preprocessing, feature engineering, and collaborate with cross-functional teams to ensure reliability, scalability, and compliance.

Lead design, development, and deployment of enterprise AI/ML solutions including AI agents, LLM integrations, MLOps/CI-CD, AWS-based data pipelines, production model serving and APIs, and integrations with enterprise systems. Provide technical leadership and mentor engineering teams.

Design, develop, and deploy ML models and pipelines; preprocess data and engineer features; implement MLOps (Docker, Kubernetes, CI/CD); collaborate with stakeholders to translate business problems into AI solutions; monitor and optimize model performance and maintain documentation and governance.

Build and operate MLOps platforms on AWS for autonomous-driving ML workloads. Maintain highly available multi-zone training and deployment environments, run distributed multi-GPU training (e.g., Ray), implement Airflow orchestration and MLflow model lifecycle, support GitHub-based CI/CD for code/models/infrastructure, and ensure reproducible, traceable, auditable ML workflows.

Design and validate performance models, optimize and debug kernel microcode and compiler algorithms, analyze runtime and cluster performance, and build tools to visualize performance data for ML/LLM inference on the Cerebras Wafer Scale Engine.

Design, build, and deploy AI systems for data loss prevention: experiment with NLP/LLM architectures, fine-tune models, implement real-time inference, develop Python and Go services, automate training/deployment, and participate in on-call operations to maintain production model accuracy and reliability.

Lead and manage a team of ML engineers, drive AI service delivery, and oversee production model deployment while fostering a high-performance culture.

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.

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.

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.

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.