AI Delivery Architect

Licensed sponsor London Full-time Posted 1 day ago

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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.

Role Summary

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.

Pre-requisites

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

Key Responsibilities

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 release gates across retrieval, prompts, models, agents, tools and end-to-end outcomes
Define end-to-end observability covering model calls, retrieval, orchestration, tools, data pipelines, latency, usage, cost, errors and audit events
Define production-readiness requirements for scalability, availability, resilience, DR, security, privacy, responsible AI and ongoing support
Validate implementation through architecture reviews, critical code and design reviews, technical walkthroughs and production-readiness checks
Resolve cross-team technical risks and work with engineers to ensure the agreed design is implemented effectively
Create reusable reference architectures, ADRs, interface contracts, engineering patterns, playbooks and operational guidance
Coach engineers through paired design and reviews, and progressively transfer architecture and operational knowledge to the wider team

Required Skills & Qualifications

Architecture & Delivery Leadership

Solution and enterprise architecture across cloud-native, hybrid and legacy environments
Architecture governance, technical design authority, ADRs and NFRs
Ability to convert ambiguous requirements into an executable design and phased delivery plan
Ability to review implementation and diagnose issues across application, data, integration and platform layers

AI, Data & Platform Engineering

LLMs, structured outputs, prompt and configuration management, grounding and common failure modes
Agentic patterns covering planning, routing, delegation, tools, state, memory, durable workflows and human approval
RAG, semantic or hybrid search, vector databases, metadata, knowledge graphs and access-aware retrieval
Data ingestion, data quality, metadata, lineage and schema evolution
Cloud services, containers, Kubernetes, IaC, CI/CD, identity, secrets, networking, logging and monitoring
MLOps or LLMOps practices covering versioning, deployment, evaluation, monitoring and rollback

AI Quality, Governance & Operations

Evaluation of groundedness, task success, policy adherence, tool selection, safety, latency and cost
Responsible AI, privacy, security, explainability, auditability and human accountability
Operational resilience, performance, capacity, incident response and production-support readiness
Metrics, logs, traces, dashboards and alerts across the complete AI service

Leadership & Professional Skills

Clear communication with business, architecture, engineering, security, risk and operations stakeholders
Decisive, evidence-based judgement in ambiguous delivery situations
Ability to surface risks and limitations without slowing execution unnecessarily
Strong collaboration, mentoring and influencing skills across distributed teams
Balanced focus on delivery outcome, engineering quality, cost, risk and maintainability

Preferred Qualifications

Architecture leadership in regulated, security-sensitive or high-availability environments
Experience creating reusable AI platform patterns, agent control planes or engineering standards
Experience with multi-model, hybrid, on-premises or sovereign AI environments
Relevant cloud solution architect certification from AWS, Microsoft Azure or Google Cloud
Relevant AI, ML, data, security, Kubernetes or service-management certification
Bachelor’s or master’s degree in Computer Science, Engineering, Data Science, Mathematics or a related field, or equivalent professional experience

What Success Looks Like

The solution design is implementable and guides delivery rather than remaining conceptual
Implementation conforms to the agreed architecture and reaches production with appropriate quality and controls
Reusable patterns reduce effort and technical risk for subsequent AI use cases
The wider delivery team can independently extend, operate and govern the solution after the incubation phase

Rewards & Benefits

TCS is consistently voted a Top Employer in the UK and globally. Our competitive salary packages feature pension, health care, life assurance, laptop, phone, access to extensive training resources and discounts within the larger Tata network.

We offer health & wellness initiatives and sports events; we are the proud sponsor of the London Marathon.

Diversity, Inclusion and Wellbeing

Tata Consultancy Services UK&I is committed to meeting the accessibility needs of all individuals in accordance with the UK Equality Act 2010 and the UK Human Rights Act 1998.

We welcome and embrace diversity in race, nationality, ethnicity, disability, neurodiversity, gender identity, age, physical ability, gender reassignment, sexual orientation. We are a disability inclusive employer and encourage disabled people to apply for this role.

As a Disability Confident Employer, we offer an interview to applicants with disabilities or long-term conditions who meet the minimum criteria for the role. Please email us at UKI.recruitment@tcs.com if you would like to opt in.

If you are an applicant who needs any adjustments to the application process or interview, please contact us at UKI.recruitment@tcs.com with the subject line: “Adjustment Request” or call TCS London Office 02031552100 / +44 204 520 2575 to request an adjustment. We welcome requests prior to you completing the application and at any stage of the recruitment process.

Next Steps:

Application Process

Online application: You can apply directly through LinkedIn/ by uploading your CV. In case you wish to submit your application via another format like audio/video, please, contact - UKI.recruitment@tcs.com
Skill-Based discussion: This will be a level 1 interview with the project team, it can be via video or in-person. Details will be confirmed by your recruiter.
Managerial discussion: This discussion will focus on behavioural aspects and person-organisation fit.
HR Discussion: This will be with one of the members of the HR team and will cover your career journey, aspirations for growth, compensation and any other questions you may have.

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Due to the high volume of applications, we will be unable to contact each applicant individually on the status of their application. If you have not received a direct response within 30 days, then it should be deemed unsuccessful on this occasion.

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