Top Machine Learning Jobs in India

The ML QA Lead ensures quality in Cerebras software across ML workloads, focusing on testing accuracy, performance, and usability. Responsibilities include driving software quality, automating workflows, debugging issues, and collaborating across teams.

The Senior Data Scientist will design and maintain data pipelines, optimize machine learning models, and collaborate with cross-functional teams to deliver impactful data-driven solutions.

Lead Easyship's data and AI strategy, scale analytics/data engineering/data science teams, enable self-serve analytics, ensure data quality/governance, drive ML initiatives and cross-functional data adoption.

Build and operate production ML infrastructure to convert prototype models (LLM, NLP, recommendation, forecasting, tabular) into low-latency, reliable services. Own CI/CD, containers, orchestration, inference microservices, feature pipelines, model/feature lifecycle, observability, safety/guardrails, and runtime reliability while collaborating with Applied Sciences and Product.

Design, build, and operate an LLM control plane/gateway and agent runtime for multi‑tenant automotive SaaS. Implement retrieval (graph+vector+keyword), MLOps pipelines, monitoring, safety/PII controls, autoscaling, and tooling so product teams can deploy and govern LLM and classical ML models at scale.

Create and curate AI/ML and data science course content, deliver instructor-led and virtual training, mentor candidates on project work, and stay current with emerging AI technologies. May require travel.

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 role involves developing data-driven applications and APIs using Python, building chatbots with LLMs, and deploying scalable solutions.

Design and deliver end-to-end GenAI and ML solutions on AWS: build SageMaker pipelines, fine-tune LLMs (Llama2), implement RAG with vector stores (OpenSearch/Elasticsearch), develop LangChain-based LLM apps, perform prompt engineering, model evaluation and optimization, and collaborate with cross-functional teams to integrate models into production.

The Associate Architect will lead the development and deployment of machine learning solutions on Azure, manage the ML lifecycle, and optimize existing models while providing technical leadership to project teams.

Lead a team of ~5 ML engineers to research, prototype, and deploy multimodal ML models for robotic inspection. Curate datasets, establish evaluation methods, fine-tune generative models (esp. vision), and improve model performance for anomaly detection and object classification within industrial sites.

Design, build, and operate scalable MLOps frameworks on GCP. Automate deployment, CI/CD, IaC, model versioning, monitoring, and lifecycle management for Python ML models using Vertex AI, GKE, Cloud Run, and related services to ensure reliable production ML services.

Design, build, and operate scalable MLOps frameworks on GCP. Automate deployment, CI/CD, IaC, model serving (Vertex AI Endpoints), monitoring, and lifecycle management for Python ML models. Ensure production reliability, scalability, security, and governance across environments.

Implement and deploy Python-based backend services and integrations for enterprise customers, build data pipelines for AI/ML workflows, integrate LLM/NLP and RAG components, ensure reliable cloud deployments, troubleshoot production issues, and collaborate with Customer Success, ML, and engineering teams to deliver scalable, productionized solutions.

Lead architecture and implementation of an agent-native, AI-powered business applications platform for ad revenue. Build end-to-end pipelines from signals to model inference, recommendations, and validated outputs. Evaluate LLMs, multimodal and multi-agent systems; deploy production ML with serving, CI/CD, monitoring, explainability, and human-in-the-loop guardrails. Partner cross-functionally to iterate and drive revenue-impacting technical decisions.

As a Machine Learning Platform Engineer at Guidewire, you'll architect and scale a secure, cloud-native ML platform, enhancing the ML lifecycle from data ingestion to model monitoring, while collaborating with cross-functional teams for efficient solutions.

As a Senior Machine Learning Engineer, you'll design, develop, and deploy advanced AI models, mentor junior members, and collaborate across teams to enhance AI capabilities in the construction industry.

As a Senior Machine Learning Engineer, you'll build LLM-powered agents, collaborate on experiments, support prompt engineering, and monitor AI features in production.

Lead the development of AI subsystems in healthcare, translating requirements into technical strategies for scalable AI/ML and computer vision solutions, ensuring quality and compliance.

Build and maintain ML infrastructure and tooling to productionize models. Collaborate with Data Science and Engineering teams to deploy, support, and evolve ML platforms and services.

Build and deploy agentic AI solutions across the full model lifecycle: design agent workflows and orchestration, implement LLM-based applications (RAG, fine-tuning), perform feature engineering and statistical validation, containerize and deploy models, create APIs, implement observability and risk mitigation, and optimize/model maintenance to deliver scalable production ML systems.

Lead architecture and development of agentic AI solutions for optical networks: design agent orchestration, memory, tool integration, guardrails, and human-in-the-loop workflows; convert processes into autonomous/semi-autonomous automation; document designs and best practices for scalable delivery.

Develop and optimize machine learning infrastructure and models for ad technology. Collaborate with teams for data analysis and engineering.

Design, develop, test, and deploy scalable GenAI, ML, and deep-learning solutions. Build and optimize RAG pipelines and LLM-powered, agentic AI applications. Create REST APIs and backend services, preprocess and integrate structured/unstructured data, fine-tune and monitor models, implement semantic search, and ensure production reliability, security, and performance while collaborating with cross-functional teams.

Lead and grow engineering teams (up to 20), set engineering strategy and roadmaps, champion agile/DevOps practices, ensure delivery of high-quality software, mentor engineers, promote systems thinking, automation, inner-source and cross-team collaboration, and ensure compliance with bank engineering standards.