AI Delivery Architect

Role: AI Delivery Architect

Job Type: Permanent

Location: London

Number of hours: 40 hours per week – full time

Ready to utilize your experience and expertise in AI Delivery Architect?

We have an exciting role for you - AI Delivery Architect!

Careers at TCS:
It means more

TCS is a purpose-led transformation company, built on belief. We do not just help businesses to transform through technology. We support them in making a meaningful difference to the people and communities they serve - our clients include some of the biggest brands in the UK and worldwide. For you, it means more to make an impact that matters, through challenging projects which demand ambitious innovation and thought leadership.

We are looking for an experienced AI Delivery Architect to design enterprise AI solutions, validate their implementation and work with engineering teams to drive execution. This is a hands‑on delivery role, not an advisory‑only architecture position. The architect converts business objectives and domain inputs into an implementable solution, makes the key technical decisions, guides the engineering team and remains accountable for the technical integrity of the delivery.

The role is important because complex AI programs bring uncertainty across models, data, integration, security, evaluation and operations. The architect provides one coherent design, makes trade‑offs visible, reduces technical risk and ensures that the resulting solution is secure, reusable, observable and ready for production.

• Technology experience, including architecture and technical delivery of enterprise solutions and recent hands‑on experience with AI‑enabled systems beyond PoC
• Strong solution architecture foundation across applications, data, APIs and integration, cloud, security, deployment and operations
• Hands‑on understanding of GenAI and agentic AI, including LLMs, RAG, context engineering, tool use, orchestration, memory and human‑in‑the‑loop (HITL) controls
• Experience designing production‑grade integrations with enterprise systems using APIs, microservices, events, streams or queues
• Experience defining evaluation, testing, observability, security, responsible AI, reliability, performance and cost controls for AI solutions
• Practical experience with at least one major cloud platform and its AI, data, identity, networking, monitoring and deployment services
• Ability to lead multidisciplinary delivery teams, review critical implementation, communicate technical trade‑offs and transfer ownership to the wider delivery organization

• Design the end‑to‑end AI solution across application, model, data, context, integration, security, deployment, evaluation, observability and operations
• Translate business objectives and domain‑team inputs into solution boundaries, architecture decisions, technical roadmaps, NFRs and measurable acceptance criteria
• Select appropriate AI patterns, models, platforms and implementation approaches based on quality, safety, latency, cost, data sensitivity, maintainability and operational risk
• Define architecture for AI agents, orchestration, tool invocation, memory, retrieval, model abstraction, governed execution and human escalation
• Define the data and knowledge approach, including ingestion, metadata, lineage, access‑aware retrieval, ranking, freshness and evidence traceability
• Define reusable integration patterns for APIs, MCP, microservices, events, enterprise systems and legacy platforms, including authentication, authorization, idempotency and failure handling
• Define standards for model gateways, routing, prompt and configuration versioning, environment promotion, release controls and rollback
• Define the evaluation strategy, test coverage, quality thresholds and…