Top Machine Learning Jobs in India

Lead full-stack development across architecture, design, implementation, testing, deployment and support. Build scalable APIs and front-end apps, lead AI agentic system design, drive SDLC and TDD, mentor engineers, troubleshoot production, and collaborate with product and stakeholders to deliver enterprise-grade features.

Develop multi-agent automation platforms, enterprise LLM integrations, RAG pipelines, knowledge bases, and domain-specific fine-tuning solutions. Build computer vision systems for manufacturing quality control, process monitoring, and assembly-line digitalization. Optimize internal finance, HR, compliance, and operations workflows. Establish enterprise AI governance frameworks, including model evaluation and prompt management.

Build and deploy production-grade machine learning systems, retrieval-driven AI agents, and LLM-powered applications. Responsibilities include developing ML pipelines, evaluating and optimizing LLM agents, working with structured and unstructured data, scaling applications, monitoring production systems, reducing technical debt, and improving the complete ML lifecycle. The role collaborates with data, product, design, and engineering teams to deliver reliable, scalable, and transparent AI solutions.

Develop scalable machine learning, generative AI, prediction, and optimization solutions for Nike’s corporate functions. Build and support advanced analytics and MLOps platforms, production applications, data pipelines, and cloud infrastructure. Collaborate with stakeholders and technology teams, influence technical strategy, mentor engineers, and deliver maintainable software using Python, AWS, containerization, CI/CD, and modern machine learning frameworks.

Lead end-to-end development and operationalization of machine learning, generative AI, prediction, and optimization solutions. Set technical direction, mentor engineers, improve engineering quality, and collaborate with stakeholders and technology teams. Build scalable applications and contribute to ML and analytics platforms using cloud, data engineering, MLOps, and software engineering practices.

Lead and build a global Data & AI platform: design scalable data architectures, implement data pipelines and MLOps, deploy and monitor ML models, apply AI to improve data quality and automation, collaborate with stakeholders, and mentor a hands-on engineering team.

Build scalable data pipelines and deploy AI models for personalization, anomaly detection, and predictive health insights. Integrate AI with consumer apps and business platforms, implement SDLC practices for continuous delivery and monitoring, deploy cloud-native containerized AI workloads on Azure/GCP with Kubernetes, and ensure consumer data privacy and compliance (GDPR/CCPA). Collaborate across BU, COE, and cloud teams and support full-stack AI-driven user interfaces.

Develop and deploy machine learning, deep learning, and computer vision solutions for image classification, object detection, segmentation, OCR, and video analytics. Prepare and annotate datasets, optimize models for production performance, collaborate on system integration, and document architectures and deployment workflows. The role also involves staying current with AI/ML research and may include real-time video processing, edge deployment, 3D vision, SLAM, and AR/VR technologies.

Senior AI/ML engineer responsible for building production-ready Python-based AI services and data-driven chatbots using LLMs and RAG. Develop and maintain FastAPI/REST APIs, data preprocessing pipelines, embedding generation, testing (Pytest), evaluation workflows, and deploy services to cloud/server environments. Collaborate with frontend teams, integrate multiple data sources and vector stores, and optimize performance and reliability.

Design, develop, deploy, and maintain ML/AI solutions for LMS and student portals. Build recommendation engines, chatbots, predictive analytics, and data pipelines. Integrate models with web apps, support UAT, troubleshooting, documentation, and coordinate with product, tech teams, and vendors to ensure reliable, secure, and scalable ML systems.

Support, analyze, validate, and calibrate market and credit risk models. Investigate market data inputs and anomalies, perform backtesting and model calibration, interpret model outputs, and collaborate with quant managers, risk teams, and developers to support risk technology platforms and publish validated results.

Design, build, and productionize AutoML and AI/ML systems. Implement containerized, cloud-native pipelines, distributed training, model evaluation, hyperparameter optimization, and knowledge-graph architectures. Collaborate cross-functionally and communicate technical concepts to business stakeholders.

Design, build, and own content knowledge platforms using Generative AI and Vision models. Implement ML models for entity matching, deduplication, summarization, tagging, visual similarity, and data-quality checks. Deploy production ML pipelines, define KPI measurement frameworks, and collaborate cross-functionally to deliver scalable, secure, and high-quality user-facing systems.

Develop, deploy, and scale end-to-end analytical solutions for the Vapor Phase Drying process. Build ML models and data pipelines, work with dashboards and model monitoring, use Python and ML libraries, leverage Azure data and ML services, apply time-series forecasting, collaborate cross-functionally, and ensure regulatory and internal compliance.

Lead architecture and hands-on implementation of production-grade Generative AI and agentic systems. Build and deploy LLM-based solutions (RAG, embeddings, agents) on cloud (primarily GCP/Vertex AI), ensure reliability, latency, cost, observability, and governance, and advise clients while mentoring engineering teams.

Design and implement agentic AI workflows and LLM-powered applications, build backend APIs and microservices, implement RAG pipelines, support ML model development and training, containerize and deploy services, and collaborate in an agile SDLC.

Research and implement new AI technologies, develop products, tools and agents, interact daily with stakeholders, and provide technical guidance to AI technologists with a forward-looking perspective.

Design, build, and deploy production-grade Generative AI platform components across GCP/Azure. Implement RAG pipelines, multi-agent architectures, LLM gateway and execution runtimes, GenAIOps/MLOps practices, AgentOps, and platform CI/CD/infrastructure. Collaborate with implementation partners and LBU teams to deliver reusable, secure, and compliant GenAI services.

Provides technical systems management and delivery support for customer environments. Responsibilities include system configuration, troubleshooting, performance monitoring and testing, documentation, and contributing to technical solution designs. The engineer works under a technical lead, collaborates with customer representatives, and supports project deliverables in an onsite setting.

Design, develop, and deploy machine learning and computer vision models (classification, detection, segmentation, OCR, video analytics). Preprocess and annotate image/video datasets, optimize models for performance and scalability, integrate solutions into production, and document architectures and workflows.

Build and maintain MLOps pipelines, deploy and serve ML models as scalable APIs, monitor model performance and data drift, develop ETL pipelines for FHIR/HL7 healthcare data, maintain feature stores, containerize workloads with Docker/Kubernetes, and ensure HIPAA/HITRUST-compliant production deployments.

Build and maintain MLOps pipelines and model-serving infrastructure for healthcare systems. Develop ETL/data pipelines (FHIR, HL7), feature stores, and CI/CD for model deployment. Ensure production reliability, monitoring, containerization (Docker/Kubernetes), cloud infrastructure, and compliance with HIPAA/HITRUST while integrating ML outputs into core applications.

Design, build, and lead deployment of production-grade AI/ML applications and pipelines (cloud or on-prem). Integrate generative AI, deep learning, chatbots, and image processing. Act as SME, mentor juniors, coordinate cross-functional teams, and ensure solution reliability and scalability.

Design, build, and maintain production-ready AI/ML applications and pipelines (cloud or on‑prem). Integrate generative AI and deep learning solutions, deploy and monitor models, optimize system performance, and mentor junior team members while collaborating with stakeholders.

Design, develop and lead production-ready AI/ML systems and pipelines (cloud and on-prem). Apply generative AI, deep learning, chatbots and image-processing models, mentor teams, and coordinate with stakeholders to deliver scalable solutions.