Azure AI ML Lead/Architect - S

Tata Consultancy Services Limited; India; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Tata Consultancy Services Limited is seeking a senior AI/ML architect to define the organization's AI/ML roadmap and build/scale Generative AI solutions on Azure. You will lead a team of AI engineers, data scientists, and cloud architects to design secure, scalable AI infrastructures and end-to-end applications using Azure services.

The role requires deep expertise in GPT models, vector databases, and responsible AI, with strong Python proficiency and cloud-native development experience.

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• 8+ years of experience in software engineering, data engineering, AI/ML, or cloud architecture.
• 5+ years of experience designing and delivering enterprise AI/ML solutions.
• Hands-on experience with Azure AI Foundry, Azure OpenAI Service, Azure Machine Learning, Azure AI Search, Azure Storage, Azure Functions, AKS, Azure DevOps.
• Experience developing AI applications using GPT models and other foundation models.
• Expertise in vector databases, embeddings, semantic search, and knowledge retrieval systems.
• Strong programming skills in Python and related AI/ML frameworks.
• Experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent.
• Knowledge of API design, microservices architecture, and cloud-native application development.
• Experience with MLOps, model lifecycle management, monitoring, and governance.
• Strong understanding of Responsible AI, model security, explainability, and data privacy.

Responsibilities

• Define and drive the organization's AI/ML architecture and technology roadmap.
• Design and implement end-to-end AI solutions leveraging Azure AI services and cloud-native architectures.
• Architect and develop enterprise-grade Generative AI applications using Large Language Models (LLMs).
• Design and implement RAG (Retrieval-Augmented Generation) solutions using Azure AI Search and vector databases.
• Develop and deploy Agentic AI systems capable of autonomous reasoning, orchestration, and task execution.
• Lead the design, training, deployment, and monitoring of Machine Learning models.
• Establish best practices for prompt engineering, model evaluation, AI governance, and responsible AI.
• Work closely with business stakeholders to identify AI opportunities and translate requirements into scalable technical solutions.
• Lead technical teams including AI Engineers, Data Scientists, ML Engineers, and Cloud Architects.
• Design secure, scalable, and cost-effective AI infrastructures on Microsoft Azure.
• Define CI/CD and MLOps practices for model development and deployment.
• Ensure compliance with enterprise security, privacy, and governance standards.
• Conduct architecture reviews, proof-of-concepts (POCs), and technology evaluations.
• Stay current with emerging AI technologies, foundation models, and industry best practices.
• Strong expertise in: Generative AI, Machine Learning, LLMs, Prompt Engineering, RAG Architecture, Agentic AI Frameworks

Skills

Generative AI

Machine Learning

LLMs

Prompt Engineering

RAG Architecture

Agentic AI Frameworks

Python

TensorFlow

PyTorch

Scikit-learn

Education

Bachelor's or Master's degree in CS/AI/Data Science/Engineering

Tools

Azure AI Foundry

Azure OpenAI Service

Azure Machine Learning

Azure AI Search

Azure Storage

Azure Functions

Azure Kubernetes Service

Azure DevOps

Job description

Key Responsibilities

• Define and drive the organization's AI/ML architecture and technology roadmap.
• Design and implement end-to-end AI solutions leveraging Azure AI services and cloud-native architectures.
• Architect and develop enterprise-grade Generative AI applications using Large Language Models (LLMs).
• Design and implement RAG (Retrieval-Augmented Generation) solutions using Azure AI Search and vector databases.
• Develop and deploy Agentic AI systems capable of autonomous reasoning, orchestration, and task execution.
• Lead the design, training, deployment, and monitoring of Machine Learning models.
• Establish best practices for prompt engineering, model evaluation, AI governance, and responsible AI.
• Work closely with business stakeholders to identify AI opportunities and translate requirements into scalable technical solutions.
• Lead technical teams including AI Engineers, Data Scientists, ML Engineers, and Cloud Architects.
• Design secure, scalable, and cost-effective AI infrastructures on Microsoft Azure.
• Define CI/CD and MLOps practices for model development and deployment.
• Ensure compliance with enterprise security, privacy, and governance standards.
• Conduct architecture reviews, proof-of-concepts (POCs), and technology evaluations.
• Stay current with emerging AI technologies, foundation models, and industry best practices.
• Strong expertise in:
  • Generative AI
  • Machine Learning
  • Large Language Models (LLMs)
  • Prompt Engineering
  • RAG Architecture
  • Agentic AI Frameworks

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• 8+ years of experience in software engineering, data engineering, AI/ML, or cloud architecture.
• 5+ years of experience designing and delivering enterprise AI/ML solutions.
• Strong expertise in:
  • Generative AI
  • Machine Learning
  • Large Language Models (LLMs)
  • Prompt Engineering
  • RAG Architecture
  • Agentic AI Frameworks
• Hands-on experience with:
  • Azure AI Foundry
  • Azure OpenAI Service
  • Azure Machine Learning
  • Azure AI Search
  • Azure Storage
  • Azure Functions
  • Azure Kubernetes Service (AKS)
  • Azure DevOps
• Experience developing AI applications using GPT models and other foundation models.
• Expertise in vector databases, embeddings, semantic search, and knowledge retrieval systems.
• Strong programming skills in Python and related AI/ML frameworks.
• Experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent.
• Knowledge of API design, microservices architecture, and cloud-native application development.
• Experience with MLOps, model lifecycle management, monitoring, and governance.
• Strong understanding of Responsible AI, model security, explainability, and data privacy.