Top Machine Learning Jobs in India

Design and review end-to-end ML architectures, build scalable models and MLOps pipelines, drive data and model lifecycle strategy, and create solution blueprints for backend and cloud-native systems while collaborating with product, data engineering, and infrastructure teams.

Design, build, and productionise ML and generative-AI systems for advertising measurement and insights. Own end-to-end model lifecycle, develop scalable pipelines, run experiments, build Gen-AI agents and semantic analytics, improve MLOps, and mentor engineers while shaping ML strategy.

Manage and operate the Databricks Lakehouse and MLOps platform, ensuring availability, security, governance and cost optimisation. Lead incident/change/service management, define MLOps frameworks and CI/CD for model lifecycle, collaborate with engineering and data science to deploy and monitor models, and manage vendors and platform roadmap.

Own the ML lifecycle from design to production: develop and optimize ML/DL models (CV, NLP, LLMs), build MLOps pipelines, deploy and monitor models in cloud/edge environments, collaborate with data scientists and product teams, and evaluate new AI technologies for engineering applications.

Design, develop, and deploy LLM-powered and Generative AI solutions including RAG pipelines, embeddings, prompt engineering, LangChain workflows, and agent systems; build AI-backed APIs and integrate models; handle data processing, vector databases, containerized deployments, basic MLOps, and implement RBAC/authentication and secure coding practices.

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, fine-tune, and optimize LLMs (LLaMA, OpenAI GPT) using LoRA and quantization; build RAG pipelines with FAISS and hybrid retrieval; develop autonomous agents with LangChain/ADK; implement ML/DL models in PyTorch and TensorFlow; build scalable APIs (FastAPI/Flask) and deploy on AWS/Azure; add guardrails, monitoring, evaluation, and performance optimization; containerize and manage deployments with Docker and Git.

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.

Lead definition and execution of AI/ML initiatives across semiconductor design flows, deploying agentic AI for frontend and backend EDA tasks. Build scalable data and model pipelines, integrate AI into engineering toolchains, measure ROI via KPIs, and grow a high-performance team while aligning with enterprise AI governance.

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.

Lead end-to-end bring-up of ML frameworks and models on Cerebras CSX systems, spanning model translation, compiler lowering, runtime integration, debugging performance/numerical issues, and prototyping tooling or API improvements to accelerate future deployments.

Drive end-to-end ML inference performance by building kernel- and system-level performance models, optimizing kernel microcode and compiler algorithms, debugging runtime and cluster performance, and developing tools to visualize performance data from the Wafer Scale Engine and compute cluster.

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.

Lead design, operation, and reliability of large-scale AI/HPC clusters. Manage day-to-day operations, incident response, automation, performance tuning, scheduler and storage optimization, and collaborate with researchers and global teams to improve GPU-accelerated infrastructure.

Build and evaluate LLM-based systems for code generation, document understanding, retrieval, and agentic pipelines. Design evaluation frameworks, work with customers and founders to shape product and roadmap, and influence engineering culture and tech stack.

Lead ABBYY's Document AI Data team, focusing on technical leadership and team management, driving data quality and scaling training data for AI models.

Design, train, optimize, and productionize deep learning models for speech/audio and computer vision. Build scalable training pipelines, perform model inference optimization, integrate models into production services, monitor performance, and collaborate with software and product teams to deliver low-latency, reliable ML features.

Develop ML pipelines, evaluate model performance, and debug issues. Generate data formats for ML models and conduct data analysis and visualization.