Top Machine Learning Jobs in India

Build and evaluate multi-agent AI systems, agentic coding tools, and statistical and machine learning models. Contribute to MLOps practices for deploying, monitoring, and maintaining production models. Analyze model performance through experiments and testing, and optimize or fine-tune existing models. The internship requires strong Python, Linux, SQL, Git, and Bash skills, plus experience with modern agentic AI tooling and machine learning frameworks.

Architect and implement enterprise-grade MLOps/LLMOps on AWS (EKS-first), building scalable pipelines for training, validation, registry, deployment, monitoring, CI/CD, observability, lineage and governance; serve as technical authority, conduct architecture reviews, troubleshoot systems, and mentor engineering teams across projects.

Develop and optimize ML models for computer vision tasks, build data processing pipelines, and collaborate on robotics integration.

Design, develop, and deploy AI/ML solutions from prototype to production, including traditional ML and modern LLM/generative/agentic systems. Build end-to-end pipelines, ensure model evaluation, fairness and reliability, collaborate with stakeholders, and meet healthcare regulatory and governance requirements.

Provide senior-level technical expertise to debug, optimise, and productionize GenAI and ML workloads on Databricks. Troubleshoot data and ML pipelines, LLM deployments, model serving, vector search, agent systems, monitoring, and cost/latency issues. Advise customers on MLOps/LLMOps, experiment tracking, model lifecycle, prompt engineering, retrieval/embedding systems, and collaborate with engineering and account teams to influence product and create documentation.

Develops advanced AI, machine learning, and computer vision algorithms for industrial visual inspection, anomaly detection, diagnostics, prognostics, and modeling. Designs experiments, analyzes large datasets, builds scalable prototypes, integrates solutions with engineering platforms, documents findings, and contributes to patents and peer-reviewed publications. Collaborates with researchers, engineers, and product managers to address complex energy-industry challenges.

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 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.

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.

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.

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

The Director of Software Engineering will lead teams in developing highly available distributed systems, focusing on payment processing, AI solutions, and innovative capabilities, while ensuring product quality and team engagement.

The Senior ML Engineer will improve Avoma's Insights Engine using NLP and Deep Learning, developing algorithms, new models, and shipping them to production.

Design, build, and deploy scalable ML models and pipelines from research to production. Own feature engineering, model tuning, real-time prediction, MLOps automation, monitoring, and lifecycle management to solve visibility, forecasting, and freight audit problems.

Lead design and build AI capabilities for security products: architect RAG/LLM pipelines and agentic workflows, train and tune ML/DL models for anomaly detection and classification, implement safety guardrails and evaluation frameworks, and establish scalable MLOps (data preprocessing to production monitoring) for low-latency, secure inferences.

Embed with customer teams to rapidly prototype, build, and deploy GenAI-powered full-stack applications and microservices, integrating Adobe/Firefly APIs, cloud and CI/CD tooling, and reusable components while capturing field use-cases for product roadmaps.

Build LLM-powered dashboards, RAG pipelines, AI agents, and data pipelines for retail operations. Implement model monitoring, fine-tuning, A/B testing, and ML lifecycle processes. Integrate cloud warehouses, vector DBs, and visualization tools; collaborate with stakeholders to translate retail KPIs into AI-driven operational workflows and deploy solutions for store ops, maintenance, NSO, and CEO escalations.

Design, build, and deploy CV and NLP ML systems for document and content intelligence. Convert prototypes to production, optimize models (including LLMs), run evaluations, build scalable APIs, and document architectures and pipelines while staying current with ML research.

Design, build, and deploy AI agents and machine learning models; integrate ML/NLP into full-stack applications; develop APIs and data pipelines; deploy and manage services on AWS; collaborate with frontend/backend teams and maintain CI/CD, databases, and production monitoring.

Design, build, deploy, and maintain production-grade machine learning models and end-to-end pipelines. Perform feature engineering, model evaluation, optimization, REST API deployment, monitoring, and automated retraining. Collaborate with cross-functional teams to translate business requirements into scalable, reliable ML services using cloud, containerization, and CI/CD practices.

Design and implement end-to-end AI/ML systems for robotic perception and control: research and build CV modules, train and benchmark models, prototype in simulation, transfer to real robots, create preprocessing/postprocessing pipelines in Python/C/C++, and optimize for real-time edge deployment.