Tcs Hiring | Cloud AI Architect - Multi Cloud | Pan India | 16 to 22 Years

Tata Consultancy Services; Bengaluru; On-site; INR 4,500,000 - 8,500,000; Full time

Job summary

Tata Consultancy Services is our client for this Pan-India Cloud AI Architect role. The candidate will lead end-to-end AI architecture across multi-cloud environments, including Azure, AWS, and GCP, with focus on RAG, MLOps/LLMOps, and governance.

This position targets senior professionals with 18–22 years of experience emphasizing enterprise-grade AI platforms. The role involves collaborating with stakeholders, ensuring secure deployment, and establishing scalable AI patterns.

Qualifications

• Enterprise-scale AI solutions across multi-cloud environments.
• Expertise in Azure OpenAI, AWS Bedrock, Vertex AI, and RAG architectures.
• 18–22 years of related experience with cloud AI platforms.

Responsibilities

• Own end-to-end architecture, technical design, and documentation.
• Translate business needs into secure, scalable AI solutions.
• Collaborate with stakeholders to define requirements, risks, and dependencies.
• Establish reusable AI patterns, governance, and best practices.
• Review designs, estimates, and deliverables for quality and readiness.
• Support governance, issue resolution, and knowledge sharing.

Skills

AWS

Azure

GCP

RAG architectures

MLOps/LLMOps

Responsible AI governance

Data privacy & security

API integration

Observability & cost

Tools

Azure OpenAI

Vertex AI

ML tooling

Job description

TCS Hiring | Cloud AI Architect - Multi Cloud | Pan India | 16 to 22 Years

TCS is hiring experienced Cloud AI Architects to lead the design and implementation of enterprise-scale AI solutions across multi-cloud environments. If you have deep expertise in Azure OpenAI, AWS Bedrock, Vertex AI, RAG architectures, MLOps/LLMOps, and AI platform governance, we would like to connect with you.

Experience: 18-22 Years

Location: Pan India

Key Responsibilities

• Own end-to-end architecture, technical design, and solution documentation.
• Translate business requirements into secure, scalable, and enterprise-grade AI solutions.
• Collaborate with stakeholders, delivery teams, and SMEs to define requirements, risks, and dependencies.
• Establish reusable AI patterns, standards, governance frameworks, and best practices.
• Review solution designs, estimates, and implementation deliverables to ensure quality and delivery readiness.
• Support technical governance, issue resolution, knowledge sharing, and continuous improvement initiatives.

Must-Have Skills

• Cloud AI services across AWS, Azure, and GCP
• AI model hosting and deployment strategies
• RAG (Retrieval-Augmented Generation) architectures
• Responsible AI and AI governance
• MLOps / LLMOps
• Data privacy, security, and compliance
• API integration and platform engineering
• Observability, monitoring, and cost optimization

Good-to-Have

• GenAI solution architecture and model evaluation
• Responsible AI controls and guardrails
• Reusable AI accelerators/frameworks
• Pre-sales solutioning experience
• Enterprise Architecture review and governance processes