Top Machine Learning Jobs in India

The AI/ML Architect designs, builds, and deploys scalable AI/ML solutions, collaborates with teams, and writes production-grade code.

As a Machine Learning Engineer at Level AI, you'll work on NLP challenges, develop scalable solutions, collaborate with teams, and innovate using cutting-edge ML techniques.

Lead strategy and development of AI/ML engineering solutions, enhancing decision-making and automation across the enterprise. Oversee advanced analytics and mentoring of data science teams.

Lead full ML lifecycle to design, build, and deploy AI/ML (including Agentic and LLM) solutions for enterprise-scale reconciliation. Analyze large financial datasets, define model roadmaps, integrate models into production, ensure scalability, manage risks, and communicate results to senior stakeholders.

The Senior Machine Learning Engineer will design, develop, and deploy AI systems, focusing on Generative AI and LLM integration across various business operations, ensuring high performance and scalability.

Build and evaluate applied NLP and LLM systems for clinical text (oncology). Develop prototypes and production components for extraction, RAG, prompts, fine-tuning, and evaluation. Collaborate with clinical data and evaluation teams, write tested Python code, analyze model failures, and improve model robustness and reliability.

Lead design, development, fine-tuning and deployment of generative AI and LLM-based solutions. Build agent architectures, RAG-enabled NLP and multimodal pipelines, integrate vector DBs and LLM APIs, collaborate cross-functionally, and research state-of-the-art GenAI and autonomous agent techniques.

Design, develop, fine-tune, and productionize ML and Generative AI/LLM solutions. Build end-to-end ML pipelines (data ingestion, modeling, deployment, monitoring), implement RAG and vector DB architectures, apply prompt/context engineering and MLOps best practices, and collaborate with cross-functional teams to optimize models for performance, latency, scalability, and cost.

Design, build, and deploy ML and generative AI applications using LLMs and RAG systems. Develop scalable APIs, manage AI pipelines and vector search, handle structured/unstructured data, train and evaluate models, and integrate AI capabilities into applications using cloud and deployment tools.

Design, build, and deploy generative AI solutions including RAG pipelines, agentic systems, and fine-tuned language models. Lead end-to-end AI projects from prototyping to cloud deployment, collaborate with cross-functional teams, and evaluate emerging AI technologies while ensuring scalable, production-grade engineering and ML ops practices.

Develop ML solutions for decision-making using advanced modeling techniques. Collaborate with teams, design evaluations, and communicate results effectively. Strong coding skills and familiarity with ML tools required.

The role involves leveraging AI and ML technologies to provide performance-based, industry-focused services, reflecting shared knowledge of industries.

KPMG seeks a Senior professional in AI/ML to leverage global expertise in technology-enabled services, providing performance-based solutions to both national and international clients.

Manage and deploy AI models, focusing on Generative AI solutions. Responsibilities include model training, cloud integration, and optimization for business applications.

KPMG India seeks a Senior AI/ML professional to provide technology-enabled services across various sectors, leveraging their expertise in AI and ML.

The role involves advanced work in AI and machine learning, focusing on data science responsibilities within KPMG, catering to various sectors.

The AI Engineer will design, develop, and deploy AI models and solutions for business problems, with responsibilities including model training, integration, and leveraging Generative AI applications.

Design and deploy AI solutions for pharmaceutical clients, collaborating with teams to develop tailored AI-driven innovations and conduct technical reviews.

As a Machine Learning Engineer Intern, you will assist in developing computer vision algorithms for item authentication and fingerprinting, collaborating on model deployment, and contributing to code quality and documentation.

Lead the design and deployment of advanced machine learning models, mentor junior staff, and drive innovation in AI technology at Ema.

Design, build, and operate high-throughput, low-latency backend services and AI/ML pipelines on an event-driven platform. Own Go services, WebSocket backends, RAG/embedding/model-serving components, and connectors to CCaaS/CRM/telephony. Work in 1-week sprints, deliver production-grade integrations or AI features, and meet strict latency and delivery commitments.

Lead development and deployment of ML-based search relevance models and pipelines. Improve query understanding, ranking, retrieval, and evaluation frameworks. Collaborate cross-functionally to productize search improvements and enable large-scale, robust search experiences.

You will design ML models to improve search and personalization, mentor junior engineers, and write maintainable code while collaborating with customers.

Senior Databricks MLOps Engineer responsible for automating the end-to-end ML lifecycle: CI/CD, packaging, deployment, scheduling, monitoring, and reproducible experiments. Build frameworks and pipelines, integrate embedding/LLM components (RAG, vector DBs, LangChain), optimize ML workloads, and collaborate with data scientists and platform teams to productionize models for Claims Payment Integrity.

Build and deploy ML models and systems powering autonomous AI agents for loan servicing and collections. Train and fine-tune LLMs, design evaluations and monitoring, tune prompts, integrate APIs, debug real-time issues, and own features from definition through production.