Sr Solution Architect

Job Title – Sr Solution Architect

Company – TCS (MEA)

Location – Dubai

Job type – Full time

About Us:

Tata Consultancy Services (TCS) is an IT services, consulting and business solutions organization that has been partnering with many of the world’s largest businesses in their transformation journeys for over 50 years. TCS offers a consulting-led, cognitive powered, integrated portfolio of business, technology and engineering services and solutions. This is delivered through its unique Location Independent Agile™ delivery model, recognized as a benchmark of excellence in software development.

A part of the Tata group, India's largest multinational business group, TCS has over 616,171 of the world’s best-trained consultants with 157 nationalities in 53 countries. For more information, visit www.tcs.com and follow TCS news at @TCS_News.

Job Description:

Key Accountabilities:

• Agentic AI Platform Architecture & Engineering:
• Design and build core components of GERNAS OS, including agent runtime, orchestration, memory, model routing, tool integration, agent registry, reusable agent services, and multi-agent orchestration patterns. Define reusable architecture for AI agents across banking domains including finance, operations, legal, risk, compliance, customer service, and wealth management. Contribute to the evolution of GERNAS OS as a secure operating layer for enterprise AI agents.
• Azure AI Services & LLM Engineering:
• Engineer solutions using Azure OpenAI, Azure AI Search, embeddings, Azure Document Intelligence, vector stores, API gateways, and model management layers. Design secure access patterns for service principals, API credentials, endpoint exposure, index permissions, and enterprise platform connectivity (e.g., Azure Data Bricks to GERNAS). Evaluate and integrate cloud-native AI services, LLM providers, model routers, and enterprise AI gateways with strong security and governance controls.
• Responsible AI, Governance & Compliance:
• Embed Responsible AI principles into the platform lifecycle, including fairness, safety, privacy, accountability, transparency, explainability, and human-in-the-loop controls. Implement guardrails, prompt defense, PII protection, model usage monitoring, audit trails, policy enforcement, and AI governance automation. Work closely with AI Governance, Model Risk, Data Governance, Information Security, Enterprise Architecture, and Compliance teams to ensure platform alignment with internal policies and regulatory expectations.
• Enterprise Integration & Tool Orchestration:
• Design and implement secure tool orchestration and MCP gateway patterns enabling governed interaction between AI agents and enterprise systems, APIs, workflows, and backend services. Define standards for tool onboarding, access control, observability, error handling, tool metadata, agent-to-agent communication, and reusable integration components. Partner with API management, integration, cloud, data platform, and application teams to industrialize AI agent connectivity.
• Platform Operations, Observability & Reliability:
• Build and operate platform observability covering traces, prompts, tool calls, latency, cost, token usage, model performance, and agent behavior. Drive DevOps, MLOps, LLMOps, and AIOps practices including CI/CD pipelines, environment management, release governance, monitoring dashboards, benchmarking, evaluation pipelines, and production support. Ensure platform resilience, scalability, cost optimization, capacity management, and operational readiness.
• Use Case Delivery & Stakeholder Enablement:
• Partner with business, technology, risk, compliance, and vendor teams to assess AI use cases and convert them into executable platform delivery plans. Guide teams on onboarding use cases, raising platform requests, using enterprise runbooks, and following approved deve