Top Machine Learning Jobs in India

Lead design and delivery of scalable, cloud-native AI and data platforms on Azure. Architect and implement generative AI/LLM/RAG solutions, optimize Spark/Databricks performance, guide teams, enforce standards, and collaborate with stakeholders to drive enterprise AI adoption in healthcare.

Provides technical leadership for enterprise Generative AI and Agentic AI initiatives. Designs scalable AI platforms, foundation-model solutions, RAG and knowledge systems, multi-agent workflows, and LLMOps capabilities. Establishes architecture standards, governance, evaluation, observability, security, and responsible AI practices while optimizing quality, performance, scalability, and cost. Leads complex delivery, mentors AI professionals, influences enterprise roadmaps, and drives adoption of reusable frameworks and shared services.

Lead GenAI tool support and enablement: triage tickets, coach non-technical users, run office hours and trainings, produce self-service guides, engage communities, track adoption themes, and coordinate escalations with technical owners.

Lead and mentor a data science team, develop ML solutions, drive AI/ML initiatives, ensure model performance and fairness, and foster innovation.

This role involves designing, building, and scaling a Data+ML platform, collaborating on machine learning pipelines, and incorporating cloud services for deployment and execution.

The posting contains only company overview and benefits; no specific responsibilities listed. A Principal Engineer typically leads technical architecture, owns complex systems, mentors engineers, drives technical strategy, and collaborates cross-functionally to ensure delivery and quality.

Lead design and production deployment of scalable AI/ML solutions: data preparation, feature engineering, model development, monitoring, drift analysis, multi-cloud infrastructure, and cross-team integration while upholding Responsible AI principles.

Lead design, development, and deployment of GenAI and NLP solutions using LLMs, vector search, and RAG. Architect agentic AI workflows, optimize inference and pipelines, perform advanced data preprocessing and feature engineering, and collaborate across research, engineering, and product teams to productionize ethical, scalable AI systems.

Lead design and implementation of advanced ML systems: architect models and services, run experiments and evaluations, deploy and scale models (MLOps), build RESTful ML APIs, and mentor junior ML engineers to integrate AI functionality across Atlassian products.

Design, architect, develop and operate end-to-end AI/ML solutions and scalable data pipelines. Lead solution roadmaps, deploy ML/DL models to cloud/edge, manage MLOps, support and maintain solutions, and perform operational tasks like data loading, batch scheduling, monitoring, and incident analysis.

Lead end-to-end delivery of enterprise AI/ML and Generative AI solutions, define architectures using LLMs and vector databases, establish MLOps and governance, mentor global teams, and drive adoption, compliance, and performance monitoring of production models.

Design, develop, and maintain full‑stack applications using C#/.NET (Core/NET6), build RESTful APIs, integrate Python-based data/AI components, implement responsive frontends, collaborate on CI/CD and cloud deployments, and apply AI/ML concepts for inference and automation.

Lead enterprise AI strategy, architecture, and delivery to scale generative AI, LLMs, agents, and reusable AI platforms. Build and run multidisciplinary teams, establish governance and MLOps/LLMOps practices, partner with stakeholders to translate business priorities into production AI capabilities, and ensure responsible, secure adoption with measurable business outcomes.

Lead design and delivery of scalable AI/ML solutions across the model lifecycle: data prep, feature engineering, modeling, deployment, monitoring, and drift detection. Architect cloud-native, multi-cloud ML infrastructure, production pipelines, observability, and experiment tracking. Mentor ML engineers and data scientists, create architecture artifacts, collaborate across product, engineering, and UX, and ensure responsible AI, reliability, and operational excellence.

Design, develop, and maintain full-stack C#/.NET web applications, collaborate with UI/UX, implement AI/ML-enabled features, write tests, optimize performance, ensure security and scalability, and participate in agile processes.

Lead the development of generative AI systems, overseeing multi-agent AI architectures, machine learning pipelines, and implementing responsible AI practices. Drive innovation in AI applications using various frameworks and databases while managing cross-functional teams.

Build, test, deploy, monitor, and maintain secure full-stack applications using Java, Spring Boot, microservices, REST APIs, Kafka, relational and NoSQL databases, and modern front-end technologies. Contribute to CI/CD, testing, observability, troubleshooting, Agile delivery, and production support. Use approved generative AI tools and develop automation or AI-enabled solutions to improve engineering productivity and business workflows while following security, privacy, and responsible AI practices.

Architect and deploy AI-powered SaaS and full-stack applications using LLMs, generative AI, agentic workflows, microservices, APIs, and cloud technologies. Integrate React or Next.js frontends with Python/FastAPI or Node.js backends and enterprise databases. Lead AI-assisted development, establish responsible AI governance, ensure security and compliance, mentor developers, review code, and translate business needs into scalable solutions.

Design, build, and productionize LLM/ML-powered detection and risk analysis systems at scale. Implement explainability, RAG pipelines, real-time/batch scoring (Kafka/Spark), optimize inference, enable experiment tracking and CI/CD, monitor drift/bias, and collaborate with product, SRE, and compliance teams.

Lead design and delivery of production-grade GenAI capabilities (RAG, embeddings, retrieval, agentic workflows). Build reusable frameworks, data pipelines, evaluation/monitoring, and governance. Advise stakeholders on technical feasibility and strategy, drive reuse and standards across teams, and mentor engineers to raise delivery and engineering practices.

Lead and grow engineering teams building large-scale AI/ML platform infrastructure (model serving, training, feature stores). Drive technical strategy, deliver complex backend systems to production, partner with product and stakeholders, uphold high engineering and operational standards, and coach engineers for career growth.

Design, build, and maintain scalable data pipelines and cloud data platforms to support AI/ML workloads. Deploy, monitor, and operationalize machine learning models, implement CI/CD and MLOps, ensure data quality, scalability, and governance while collaborating with data scientists, engineers, and stakeholders.

Lead and grow an ML engineering team to design, build, and operate a unified AI/ML data collection platform. Own data ingestion, feature management, LLM/RAG workflows, model lifecycle, evaluation, observability, scalability, governance, and cross-functional alignment while mentoring engineers and driving platform reliability and operational excellence.

Design, develop, and deploy AI/ML models (including NLP and LLM solutions) for Sales and Digital use cases; analyze data, build model APIs, collaborate cross-functionally, and translate AI research into production-ready capabilities while ensuring ethical, transparent model development.

Design, develop, and operationalize AI/ML, Generative AI, LLM, and agentic AI solutions. Build scalable batch, streaming, and cloud-native data pipelines using PySpark/Scala on Azure. Implement MLOps, CI/CD, model monitoring, RAG/vector search, and production-grade LLM applications. Collaborate with data science, architecture, and business teams, evaluate emerging AI tech, and produce prototypes, POCs, and documented deployments.