Top Machine Learning Jobs in India

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.

The Senior Software Engineer will lead the design and development of AI-driven solutions, mentor junior engineers, and optimize AI/ML models for various applications.

Design and validate performance models, optimize and debug kernel microcode and compiler algorithms, analyze runtime and cluster performance, and build tools to visualize performance data for ML/LLM inference on the Cerebras Wafer Scale Engine.

This role involves designing scalable systems for Generative AI, integrating Azure OpenAI, and mentoring development teams in AI projects.

Design, build, and deploy AI systems for data loss prevention: experiment with NLP/LLM architectures, fine-tune models, implement real-time inference, develop Python and Go services, automate training/deployment, and participate in on-call operations to maintain production model accuracy and reliability.

Design and implement software solutions with cross-functional teams; produce technical specifications and documentation; write, test, deploy code; perform code reviews; provide technical support; collaborate on QA/testing; stay current with tech trends and recommend integrations.

Lead architecture and engineering of enterprise-scale Generative AI and data platforms. Design reusable platform services for end-to-end AI/ML lifecycle, build agentic applications and event-driven systems, enforce CI/CD and DevSecOps, ensure security/governance/observability, mentor engineers, and drive platform modernization using cloud-native, containerized, and distributed technologies.

Lead platform automation for enterprise Generative AI and data platforms: design and build IaC, CI/CD, observability, deployment automation, and self-service capabilities to accelerate AI/ML lifecycle, reliability, governance, and developer productivity across cloud-native distributed environments.

Lead end-to-end design, build, deployment and iteration of AI-agent solutions. Embed with clients to map workflows, architect multi-agent systems, integrate enterprise systems and RAG pipelines, implement guardrails and observability, and liaise with non-technical stakeholders to drive adoption.

Design, automate, and manage end-to-end ML pipelines and scalable infrastructure on Azure. Build CI/CD with GitHub Actions, containerize models with Docker and AKS, monitor model lifecycle and data drift, implement cost controls, and collaborate with data science, DevOps, and IT to deploy resilient, production-grade ML solutions.

Lead and manage a team of ML engineers, drive AI service delivery, and oversee production model deployment while fostering a high-performance culture.

The Senior AI/ML Engineer will design and operate machine learning systems in healthcare revenue operations, collaborating with teams to enhance efficiency and reduce costs. Responsibilities include developing ML solutions, setting up testing dashboards, mentoring junior engineers, and optimizing document analysis processes.

Own the end-to-end operational lifecycle of ML and LLM systems: build CI/CD for training and serving, deploy and maintain models, run monitoring/drift detection, operate LLM workflows (prompt versioning, evaluation, guardrails), and ensure reproducibility, reliability, and security across cloud, on-prem, and edge environments.

Lead design and implementation of scalable, secure AI/ML architectures; develop, train, evaluate, and optimize models (NLP, CV, predictive analytics); productionize multi-agent and GenAI solutions on cloud; pilot prototypes, perform A/B testing, conduct code reviews, and collaborate with stakeholders to align solutions with business needs.

Build and maintain scalable ML pipelines and infrastructure, integrate LLMs, manage model lifecycle with MLFlow, collaborate in agile teams, write high-quality code, perform code reviews, deploy and monitor models in cloud environments.

Build, deploy, and maintain scalable ML pipelines and production models. Integrate LLMs into applications, manage model lifecycle with MLFlow, collaborate with data scientists and engineers, participate in Agile/Scrum, ensure code quality through reviews and CI workflows, and resolve infrastructure and deployment issues.

Design, build, and scale MLOps/LLMOps pipelines and secure automated deployments on Azure. Ensure governance, observability, and evaluation of production models. Partner with data scientists and product teams to operationalize AI, implement safe rollouts (A/B tests, canaries, human-in-the-loop), and maintain model performance and compliance.

Research, develop, and productionize large-scale ML and Generative AI capabilities (LLMs, embeddings, RAG, multimodal, ranking). Build training, fine-tuning, evaluation, and feedback-driven learning systems (RL, bandits). Design experiments, analyze model behavior and failures, and collaborate with engineering and product teams to deliver scalable, maintainable AI features with measurable customer impact.

Lead AI projects by translating business needs into technical solutions, developing AI applications, and integrating AI capabilities across platforms. Oversee large-scale digital transformation initiatives while mentoring teams and ensuring efficient SDLC processes.

Lead and manage AI/ML projects and teams for KPMG India, delivering technology-enabled services to national and international clients while leveraging global network and local market knowledge.

Research, prototype, and integrate LLMs and NLP solutions into business use cases. Experiment with and fine-tune ML/DL models, optimize performance, build data pipelines, and collaborate with cross-functional teams to deploy LLM-based applications.

Convert data‑science prototypes into production ML services; build and operate Databricks/Spark pipelines reading/writing Snowflake; manage full model lifecycle (MLflow, CI/CD, retraining, drift monitoring); deliver/version audience segments to ad‑tech partners; ensure scalability, cost efficiency, privacy, and mentor engineers.

The Software Machine Learning Test Engineer will develop test automation for AI components, improve test infrastructure, and ensure software quality for machine learning models using Python and GitLab CI/CD.