Enterprise Architect

This is a senior strategic role within the Enterprise Strategic Architecture practice, focused on defining and delivering next‑generation digital transformation programs for leading global organisations. The successful candidate will bring together deep technology expertise and strong business acumen to help clients navigate complex, large‑scale modernisation initiatives. As AI becomes central to how enterprises transform, this role is expanding in scope: the architect must be equally comfortable designing cloud‑native platforms, structuring human and agent collaborative workflows, and embedding AI‑driven capabilities as first‑class components of the overall solution. You will collaborate closely with sales and delivery teams across the full program lifecycle — from shaping solutions during presales through to governing technical quality in delivery. You will engage with CDOs, CTOs, and senior digital leaders at client organisations, contribute to industry thinking through published viewpoints and speaking engagements, and play an active role in identifying emerging technology opportunities that can be developed into compelling propositions for the market.

Responsibilities

• Strategic Thinking: Candidate can articulate where AI agents replace human tasks vs. augment them in a $10M+ transformation context. Can draw a human+agent operating model for a business process — showing handoff logic, oversight points, and accountability chains. Understands that LLM inference is now a line item in program budgets and can estimate it at ROM level for a given use case volume.
• Design Depth: Has 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. Can explain prompt architecture decisions (system prompt structuring, context compression strategies, few‑shot vs. zero‑shot tradeoffs) and how these affect both quality and cost. Understands model selection tradeoffs – when to use frontier models vs. fine‑tuned smaller models vs. cached completions.
• Token Optimization Fluency: Has operationalised token efficiency at scale — structured prompt libraries, semantic caching, chunk sizing for RAG pipelines, output length controls, batching strategies. Can model cost‑per‑transaction for an AI‑enabled workflow and present that as part of a business case. Understands how token spend interacts with context window limits across model families (GPT‑4o, Claude, Gemini) and can 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 — 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
• Modern CI/CD & delivery pipelines
• 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, 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 program 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, and 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
• Should be excellent planner when it comes to perform release planning and other delivery planning.
• Should have excellent problem‑solving skills.
• Responsible for Coaching and mentoring team members.
• BFSI/FS Domain exp.

Personal Traits

• 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.