Job Description

AI Leader Industry Cloud & Digital (ICD)

AI Architecture & Hands-on Engineering Expertise (Mandatory)

This is a hands-on architect-leader role. Candidates must have personally designed, built, and shipped production AI systems, not only overseen them.

Level:  Senior Leader – Client-facing and Internal transformation leadership

Location Flexible (aligned to ICD Sectors); travel as required for key pursuits and strategic accounts

Role Overview

The AI Leader in ICD is a senior, hands-on AI architect, client‑facing technologist and transformation leader accountable for designing, building, and scaling enterprise AI solutions, accelerating AI adoption, realizing measurable business value, and embedding AI into deal strategy and commercial constructs across A1 accounts.

The role partners directly with CIOs, CTOs, COOs, Business Leaders, Sector leaders and ICD team to:

• Embed AI into software engineering, modernization, quality engineering, and SRE
• Architect and build production-grade agentic AI solutions, spanning multi-agent orchestration and interoperability, secure integration of enterprise tools and data, and governed, cost-aware access to models
• Shape AI‑led transformation roadmaps and commercial models
• Ensure AI initiatives move beyond experimentation to repeatable, governed value realization

This role bridges strategy, architecture, hands-on execution, and outcomes, ensuring AI becomes a trusted, sustainable lever for productivity, quality, speed, and resilience.

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Core Responsibilities

1. Driving Enterprise AI Adoption

AI Strategy & Roadmap

• Co‑create with clients and Delivery team, AI adoption roadmaps aligned to client business priorities aligned to WEGA archetypes
• Identify and prioritize high‑impact AI use cases (e.g., engineering productivity, accelerated modernization, intelligent quality, AI‑driven SRE).

AI‑Enabled Delivery Transformation

• Embed AI into end‑to‑end software delivery workflows—from requirements to code generation, quality engineering, DevSecOps & SRE.
• Introduce agent‑based and AI‑assisted execution models where they deliver clear value.
• Establish delivery standards, reference architectures, and guardrails to ensure quality and security.

Change Enablement & Adoption

• Support organizational change by helping clients shift ways of working, roles, and skills to normalize AI in daily execution.
• Enable leadership and teams with structured adoption approaches, role‑based enablement, and best‑practice patterns.

Measurement & Value Realization

• Define success metrics and dashboards to track AI adoption and business impact.
• Help clients continuously optimize AI initiatives based on measurable outcomes.

2. Shaping AI‑Led Transformation Programs and Deals

AI‑Led Solution & Transformation Design

• Partner with client leadership to shape AI‑driven transformation programs across modernization and managed services.
• Translate AI capabilities into clear business cases and transformation narratives.

Commercial & Value Structuring

• Design commercial models that reflect AI‑driven productivity and outcomes, including:
• Outcome‑based constructs
• Productivity‑linked pricing
• Gain‑share and shared‑value models
• AI‑enabled service bundles
• Ensure AI benefits are explicitly defined, measured, and fairly shared.

Risk, Security & Responsible AI

• Address enterprise concerns around data security, residency, governance, and auditability.
• Ensure AI initiatives align with responsible AI principles and regulatory expectations.

Deal‑to‑Delivery Inception

• Ensure commitments made during strategy and contracting are seamlessly embedded into delivery execution, governance, and reporting—avoiding gaps between promise and realization.

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Client Engagement & Leadership

• Act as a trusted AI advisor to senior client stakeholders.
• Facilitate executive discussions on AI strategy, adoption readiness, and transformation trade‑offs.
• Provide thought leadership on emerging AI and agentic delivery patterns relevant to client industries.

Leading a Team

• Leading a team of FDE’s and AI thought leaders to drive client and business outcomes
• Create pov’s and assets that can be leveraged cross sectors
• Author and publish white papers to drive eminence for Wipro in the market
• Create rapid PoC’s in client environment to showcase value

Experience & Expertise

Clients can expect a leader who brings:

• Deep experience in enterprise AI transformation
• Proven success delivering AI‑enabled engineering and operations outcomes
• Strong understanding of commercial and contractual implications of AI
• Ability to translate complex AI concepts into clear business value narratives
• Experience working with regulated and security‑sensitive environments

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AI Architecture & Hands-on Engineering Expertise (Mandatory)

This is a hands-on architect-leader role. Candidates must have personally designed, built, and shipped production AI systems, not only overseen them. Required experience includes:

• Hands-on AI engineering (must-have): Recent, personal, hands-on experience designing, coding, and deploying LLM-based applications and agents into production. The candidate must be able to build PoCs in client environments, debug model and agent behaviour directly, review code from FDEs, and lead the team by doing, not only by directing.
• Enterprise AI architecture: Proven track record designing end-to-end AI reference architectures for large enterprises, covering model selection and routing, orchestration, context and data layers, integration with existing enterprise systems, identity, security, and observability, with defensible trade-offs across cost, latency, accuracy, and risk.
• Agent-to-Agent (A2A) protocol and multi-agent systems: Experience architecting multi-agent systems using A2A, including orchestrator and sub-agent decomposition, agent discovery via agent cards, task delegation and hand-offs across agents and vendor boundaries, long-running task management, and agent registries.
• Model Context Protocol (MCP): Hands-on experience building and operating MCP servers and clients that expose enterprise tools, data, and APIs to agents, including tool schema design, OAuth-based authentication and authorisation, tool-level permissioning, and governance of enterprise MCP server catalogues.
• AI Gateways: Experience architecting and implementing AI/LLM gateways for centralised, governed model access, including multi-provider routing and fallback, rate limiting and quota management, token cost tracking and chargeback, semantic caching, PII redaction, prompt and response guardrails, and audit logging.
• Retrieval and context engineering: Deep practical knowledge of RAG architectures (chunking, embeddings, vector and hybrid search, re-ranking), knowledge graph and GraphRAG patterns, and context engineering for agents, including context window management, memory, prompt caching, and structured agent instructions.
• Agentic frameworks and AI-assisted SDLC tooling: Working experience with agent frameworks and SDKs (e.g., LangGraph, Claude Agent SDK, OpenAI Agents SDK, Google ADK, Semantic Kernel, CrewAI) and with AI coding assistants and autonomous coding agents applied across the SDLC.
• LLMOps, evaluation, and observability: Experience establishing evaluation harnesses (golden datasets, LLM-as-judge, regression testing of prompts and agents), tracing and observability for agentic workflows (e.g., OpenTelemetry-based), and production monitoring for quality, drift, and cost.
• Model adaptation and optimisation: Strong grasp of prompt engineering, fine-tuning and parameter-efficient techniques (e.g., LoRA), model tiering between frontier, open-weight, and small language models, batch inference, and self-hosted versus API economics, with a disciplined approach to token cost governance.
• AI security, safety, and governance controls: Experience implementing human-in-the-loop approval gates, guardrails, and defences against prompt injection and tool misuse; threat modelling for agentic systems (e.g., OWASP Top 10 for LLM Applications); and alignment with frameworks such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001.
• Cloud AI platforms: Hands-on experience with at least one hyperscaler AI platform (Azure AI Foundry, AWS Bedrock, or Google Vertex AI) and with the APIs of leading model providers.

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What Clients will Gain

Clients engaging with the AI Technology Partner benefit from:

• Practical AI adoption at enterprise scale—not pilots or proofs of concept
• Faster time‑to‑value through AI‑enabled SDLC and intelligent automation
• Clear business outcomes tied to productivity, quality, and cycle‑time improvement
• Confidence and trust through secure, compliant, and responsible AI adoption
• Continuity from strategy to execution, with no disconnect between ambition and delivery