Senior Enterprise Architect-Enterprise Architect/Segment lead-UK

This senior strategic role within the Enterprise Strategic Architecture practice focuses on defining and delivering next‑generation digital transformation programmes for leading global organisations. The successful candidate brings deep technology expertise and strong business acumen to help clients navigate complex, large‑scale modernisation initiatives. As AI becomes central to enterprise transformation, the role expands in scope: the architect must design cloud‑native platforms, structure human‑and‑agent collaborative workflows, and embed AI‑driven capabilities as first‑class components of the overall solution.

Collaboration with sales and delivery teams spans the full programme lifecycle — from shaping solutions during presales to governing technical quality in delivery. The architect engages with CDOs, CTOs, and senior digital leaders at client organisations, contributes to industry thinking through published viewpoints and speaking engagements, and identifies emerging technology opportunities for compelling market propositions.

Responsibilities

• Strategic Thinking – Articulate where AI agents replace or augment human tasks in $10M+ transformation contexts, and design a human+agent operating model for a business process, including handoff logic, oversight points, and accountability chains.
• Understand LLM inference as a line item in programme budgets and estimate it at ROM level for a given use‑case volume.
• Design Depth – Have personally designed or reviewed an agentic system in production (e.g., a multi‑step reasoning pipeline, an autonomous code‑review agent, or a RAG‑powered enterprise knowledge layer).
• Explain prompt architecture decisions (system prompt structuring, context compression strategies, few‑shot vs. zero‑shot trade‑offs) and how they affect quality and cost.
• Understand model selection trade‑offs – when to use frontier models, fine‑tuned smaller models, or cached completions.
• Token Optimization Fluency – Operationalise token efficiency at scale through structured prompt libraries, semantic caching, chunk sizing for RAG pipelines, output length controls, and batching strategies.
• Model cost‑per‑transaction for an AI‑enabled workflow and present it as part of a business case.
• Understand how token spend interacts with context window limits across model families (GPT‑4o, Claude, Gemini) and make architecture trade‑offs accordingly.

Must Have Skills

• Agentic architecture design – Multi‑agent orchestration, tool‑use design, human‑in‑the‑loop checkpoints, agent failure modes, and recovery.
• Human + agent workflow design – Task decomposition across human and AI agents; escalation paths; accountability mapping in regulated environments.
• Expertise in leveraging coding agents – GitHub Copilot, Claude, Devin.ai, and similar tools to accelerate software delivery within a structured, governed engineering lifecycle.
• Design and governance of automated delivery pipelines using tools such as Harness, GitHub Actions, ArgoCD, and Tekton; trunk‑based development, progressive delivery, and release automation.
• Full‑stack application development – Architecture and delivery of modern full‑stack applications; proficiency across frontend frameworks, API layers, backend services, and data tiers at enterprise scale.
• High‑scalability integration – Architecting event‑driven and streaming integration at scale using Apache Kafka and Kafka Streams; asynchronous messaging patterns, schema registries, and real‑time data pipelines across distributed systems.
• NoSQL & enterprise data platforms – Design of polyglot persistence architectures spanning NoSQL stores (MongoDB, Cassandra, DynamoDB), enterprise caching layers (Redis, Hazelcast, Memcached), and search platforms (Elasticsearch, OpenSearch).
• Hyperscaler resilience patterns – Building highly available, fault‑tolerant solutions on AWS, Azure, and GCP; multi‑region active/active, chaos engineering, SRE practices, availability zone failover, and disaster recovery at cloud scale.
• Token economics & LLM costing – Prompt compression, context window sizing, model tier selection, and cost‑per‑transaction modelling at enterprise scale.
• AI TCO & commercial modelling – Inference cost projections, build‑vs‑buy for foundation models, ROI framing for AI‑augmented delivery.
• Digital transformation leadership – AI‑native program design spanning cloud, integration, agentic capability layers, and responsible AI governance.
• Enterprise integration patterns – Streaming, API, event‑driven, and real‑time patterns extending to RAG, vector stores, embedding services, and LLM APIs as first‑class integration nodes.
• Chief Architect leadership – Governing cross‑domain architect teams while managing AI risk, hallucination mitigation, and responsible AI policy at programme level.
• Multi‑cloud architecture (15+ yrs) – Hybrid IaaS/PaaS, multi‑AZ/region, IaC automation first, DevSecOps, K8s orchestration.
• Influencing & stakeholder leadership – Builds and sustains networks across organisational boundaries through credibility and influence rather than authority; aligns diverse stakeholders (engineering, business, executive) around a shared technology direction and drives teams to deliver outcomes in complex, matrixed environments.
• CXO communication – Articulates at the right level of abstraction and detail from developer to board level.

Preferred

• Excellent release and delivery planning skills.
• Strong problem‑solving ability.
• Experience coaching and mentoring team members.
• Domain experience in BFSI/FS.

Personal

• High analytical skills.
• High customer orientation.
• High quality awareness.

All aspects of employment at Infosys are based on merit, competence and performance. We are committed to embracing diversity and creating an inclusive environment for all employees. Infosys is proud to be an equal opportunity employer.