AI Lead / AI Architect (NCS/Job/ 4102)

Job Description

The Role

We are looking for a battle-tested AI Engineering Leader who sits at the intersection of delivery excellence and intelligent automation. This is not a role for generalists — it demands someone who has shipped large-scale technology programmes across industry verticals, built or modernised cloud-native platforms, and is now leaning into AI to drive measurably faster, smarter delivery. You will anchor a delivery-led growth motion, working hand-in-glove with practice, consulting, and client partner leaders to expand the portfolio while keeping quality non-negotiable.

 

 

What You Will Do

Delivery & Programme Leadership

▪ Own end-to-end delivery of a $10M+ engineering portfolio across clients — on time, on budget, and to quality bar.

▪ Lead platform build, modernisation, and custom application programmes natively on cloud, spanning .NET Full-Stack, Java Distributed Systems, Python stack etc.

▪ Set and enforce engineering standards: architecture guardrails, code quality, DevSecOps, and release cadence across multi-team engagements.

▪ Manage programme risk proactively — escalate early, resolve decisively, and keep clients informed throughout.

AI-Driven Engineering Acceleration

▪ Embed AI tooling across the SDLC — from AI-assisted requirements and design through to automated testing, code generation, and incident response.

▪ Architect and operationalise agentic systems and workflows that reduce manual toil, accelerate delivery cycles, and improve output quality.

▪ Quantify the impact of AI adoption: establish baselines, track velocity and quality metrics, and present measurable efficiency gains to clients and leadership.

▪ Stay ahead of the AI tooling curve; evaluate and pilot emerging platforms (LLM orchestration, RAG pipelines, AI code assistants).

Portfolio & Revenue Growth

▪ Carry full P&L accountability for the portfolio — margin, revenue, forecasting, and commercial hygiene.

▪ Partner with practice, consulting, and client partner leaders to identify expansion opportunities within existing accounts and shape new pursuit strategies.

▪ Translate delivery track record into growth narrative — contribute to proposals, solution designs, and client presentations that differentiate on execution credibility.

Client & Stakeholder Engagement

▪ Serve as the senior delivery point-of-contact for clients — build trust-based relationships at CTO/CIO/VP level.

▪ Facilitate governance forums (steering committees, QBRs, escalation calls) with clarity and confidence.

▪ Align internal stakeholders — practice heads, resource managers, people leaders — to programme needs without bureaucratic drag.

People & Capability Development

▪ Lead, mentor, and grow a high-performing engineering organisation; foster a culture of ownership and continuous improvement.

▪ Champion individual upskilling — create structured learning pathways around AI, cloud, and modern engineering practices.

▪ Spot and develop next-generation delivery leaders from within the team.

 

 

What You Bring

Experience & Background

▪ 15–17 years in software engineering with a significant portion in leadership roles managing multi-team, multi-million-dollar programmes.

▪ Hands-on track record of delivering platform build, legacy modernisation, and greenfield application programmes on cloud — not just oversight, but technical depth you can draw on in client conversations.

Technical Stack & Architecture

▪ .NET Full-Stack (C#, ASP.NET Core, Azure-native services) and/or Java Distributed Systems (Spring Boot, microservices, Kafka, Kubernetes) — you can assess architecture quality, not just read status reports.

▪ Python stack experience (FastAPI, Django/Flask, pandas, NumPy) particularly for data pipelines, AI/ML integrations, and automation scripts.

▪ Cloud-native delivery on Azure, AWS, or GCP; Infrastructure as Code, CI/CD pipelines, container orchestration, and observability are second nature.

▪ Practical experience designing and deploying agentic AI systems — LLM orchestration, tool-use patterns, retrieval-augmented generation, and multi-agent workflows in an enterprise context.

AI & Automation Fluency

▪ Hands-on experience with enterprise AI coding and productivity tools — GitHub Copilot / Claude (Anthropic), and / or Cursor — applied meaningfully across design, development, review, and documentation phases of the SDLC.

▪ Understands where AI drives automation, acceleration, and efficiency within IT application landscapes — and equally where it introduces risk that must be managed, especially in regulated domains.

▪ Ability to differentiate between AI hype and production-ready tooling; pragmatic evaluator of what to adopt, when, and how.

Leadership & Commercial Acumen

▪ Proven P&L ownership at $10M+ scale — comfortable with revenue forecasting, margin management, SOW negotiations, and change order governance.

▪ Excellent stakeholder management with both internal leaders and senior client executives; able to hold a room, manage difficult conversations, and build long-term advisory relationships.

▪ Growth mindset — actively invests in own learning and models the same for the team