Top Machine Learning Jobs in India

The Sr ServiceNow Engineer leads the development and configuration of solutions within ServiceNow, focusing on HR Service Delivery. Responsibilities include designing functional solutions, custom application development, and maintaining compliance with business requirements.

Design, develop, deploy, and maintain GenAI-based cloud applications and platforms. Lead solution design, coding, testing, integration and troubleshooting in containerized, Kubernetes-managed environments. Mentor engineers, ensure security and compliance, drive adoption of new technologies, and collaborate across teams to deliver scalable, cloud-native AI/ML services.

Design, develop, and deploy scalable AI/ML solutions: research and implement models (deep learning, transformers, RL), perform data preprocessing and feature engineering, productionize and monitor models, mentor team members, and translate business requirements into technical solutions.

Design, develop, and deploy scalable AI/ML solutions and deep learning models. Lead research, model selection, training, hyperparameter tuning, production deployment, monitoring, and cross-functional collaboration. Provide technical leadership, mentor junior engineers, and translate business requirements into robust ML systems.

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 AI and Machine Learning Engineer develops software algorithms to analyze data, improves system performance using machine learning techniques, and mentors junior team members while collaborating with stakeholders on technical solutions.

Design, develop, deploy, and monitor production-grade AI/ML and GenAI cloud applications. Build data pipelines, train and evaluate ML/DL models, implement MLOps/LLMOps, integrate LLMs, RAG, and vector search, containerize and deploy services, and collaborate with cross-functional teams to deliver secure, scalable solutions.

Design, develop, and deploy advanced machine learning and deep learning models; lead research, architecture, and productionization of AI solutions; mentor teams; monitor and optimize model performance; collaborate with cross-functional stakeholders to align AI/ML systems with business requirements.

Design, build, and maintain scalable data pipelines and feature engineering workflows. Deploy and monitor ML models for real-time and batch inference, create secure REST APIs for model serving, integrate ML services with systems, troubleshoot production issues, and collaborate with data scientists and engineers to deliver cloud-native ML solutions.

Contribute to research, prototyping, and building computer vision and generative AI models. Develop and evaluate deep learning architectures, handle data preprocessing and augmentation, build reproducible training/inference pipelines with PyTorch or TensorFlow, run experiments, conduct literature reviews, and collaborate with cross-functional teams. Participate in code reviews and knowledge sharing.

The Machine Learning Engineer role involves designing, building, and operating ML systems, collaborating with data scientists, and ensuring governance in production environments. The engineer will manage the ML lifecycle and contribute to the technical strategy and MLOps maturity.

The role involves solving customer problems by building ML solutions, engaging with various stakeholders, and taking ownership of the outcome through deployment.

The role involves managing ML models, deploying them in production, evaluating technologies, and implementing CI/CD processes while supporting model development and ensuring data security.

Lead and scale an AI engineering team to design, train, optimize, and deploy deep learning models for 2D/3D medical imaging. Oversee model architecture, MLOps pipelines, clinical integrations (PACS/RIS), and optimize models for cloud, edge, and clinical hardware while mentoring engineers and ensuring regulatory and data privacy considerations.

Build, deploy, and operate production-grade ML systems and retrieval-driven LLM agents. Design and evaluate LLM-based agents, scale ML applications, reduce technical debt, collaborate across teams, and drive the full ML lifecycle from data to production monitoring.

Lead and scale an AI engineering team to develop, train, optimize, and deploy deep learning models for 2D/3D medical imaging. Drive model architecture choices (CNNs, Transformers, U-Net variants), implement MLOps pipelines, ensure cloud/edge clinical deployment, and collaborate with clinicians to integrate solutions into PACS/RIS workflows while meeting data privacy and compliance needs.

The Staff Machine Learning Engineer will lead the development of ML models, ensure data quality, deploy solutions, and mentor junior engineers to enhance fraud detection.

Develop and deploy AI solutions for virtual tire development, perform large-scale data exploration, feature engineering, modeling, visualization, and productionize models with cross-functional teams to improve engineering workflows.

No responsibilities are provided in the job description. The title "Cloud Machine Learning Engineer" implies work on developing, deploying, and maintaining machine learning models and infrastructure in cloud environments.

Research and develop ML/AI models for financial markets: fine-tune LLMs, build benchmarks, analyze large noisy time-series market data, perform feature engineering, validate models on GPU clusters, and translate research into deployable trading signals while collaborating with researchers, traders, and engineers.

Design, build, deploy, and monitor LLM-based GenAI applications and RAG/document-intelligence solutions. Implement agentic/multi-agent systems, backend AI services, chat UIs and copilots, integrate vector databases, and productionize models on cloud platforms ensuring performance, security, scalability, and Responsible AI compliance.

Lead architecture, development, and deployment of scalable ML and Generative AI solutions. Own end-to-end model lifecycle, MLOps pipelines, cloud-native deployments (AWS), vector DB ingestion, LLM/embedding selection, CI/CD, and mentor junior engineers while collaborating with cross-functional teams.

Design, prototype, and deploy AI/ML and computer-vision solutions for production factories. Perform feasibility studies, collaborate globally with research teams, support operational excellence, and integrate systems with existing hardware to improve production processes.

Design and develop machine learning infrastructure and models, support product teams, build scalable data services, and promote collaboration within a diverse engineering team.

This role involves developing scalable AI/ML models, working with cross-functional teams, following agile processes, and maintaining deployed solutions.