Technical Delivery Lead (AI-SDLC)

Opis stanowiska

About the Role

As artificial intelligence shifts from experimental pilots to core enterprise architecture, leading organisations require digital engineering partners who can embed AI directly into the Software Development Life Cycle (SDLC). This requires moving beyond traditional software delivery to operationalise Agentic Workflows, Generative AI tooling, LLMOps, and automated AI-driven engineering practices at scale.

As a Technical Programme Manager (AI SDLC) based in the UK, you will serve as the senior delivery authority accountable for leading multi-million-pound engineering programmes. You will drive both the creation of AI-powered software products and the transformation of enterprise SDLCs using modern AI tooling (Copilots, automated code generation, AI testing, and continuous delivery pipelines).

Requirements

Domain & Technical Expertise

• AI SDLC & LLMOps Fluency: Deep understanding of how AI integrates into modern software engineering—including LLMOps, RAG patterns, Agentic frameworks, fine-tuning vs. prompt engineering, vector storage, and model observability tools.
• Modern SDLC & Tooling Mastery: Expertise in enterprise DevOps/DevSecOps, CI/CD automation, trunk-based development, and AI coding assistants (e.g., GitHub Copilot, Tabnine, Amazon Q).
• AI Security & Governance: Strong grounding in AI ethics, IP guardrails, security standards for AI-generated software, and compliance with emerging AI regulations.

Programme & Delivery Leadership

• Large-Scale Delivery Track Record: 15+ years in Digital/Software Engineering or IT Consulting, with proven success leading complex, fixed-outcome programmes (£10M+ annual revenue) and managing distributed teams of 100+ across global delivery centres.
• Agile Software Metrics: Expertise in tracking and improving engineering health using DORA metrics, SPACE framework, and productivity tracking tailored for AI-augmented teams.
• Commercial & Risk Rigour: Strong financial governance (P&L management, margin optimisation, commercial risk mitigation) for complex client engagements.

Strategic Influence & Communication

• Executive Advisory: High executive presence to bridge business goals, technical execution, and engineering culture, acting as a trusted advisor to client CTOs, CIOs, and VPs of Engineering.
• Value & Impact Orchestration: Ability to articulate the business case and ROI for AI adoption in software delivery through clear, metric-driven communication.

Academic Background: Degree in Computer Science, Software Engineering, Artificial Intelligence, or a related quantitative field preferred.

Job Responsibilities

Delivery & AI Engineering Oversight

• End-to-End AI SDLC Delivery: Drive the full lifecycle of complex software programmes incorporating AI (Custom LLM solutions, RAG architectures, Agentic Workflows, and AI-powered SDLC tooling) from discovery to production rollout.
• AI-Assisted SDLC Transformation: Lead initiatives to integrate AI tools (GitHub Copilot, Cursor, automated test generators, static analysis tools) into client software pipelines, measuring and accelerating developer productivity and code quality.
• Architecture & Technical Alignment: Interrogate architectural decisions across the AI software stack (LLMOps pipelines, fine-tuning frameworks, vector databases, cloud infrastructure) to ensure solutions are scalable, maintainable, and secure.
• Pre-Sales & AI Solutioning: Lead technical pre-sales engagements, architecting Statements of Work (SOWs) and solution proposals for AI platform engineering and software transformation engagements that are commercially sound and technically viable.
• Model Evaluation & Quality Assurance: Establish governance for AI software quality, including automated evaluation frameworks (evals), regression testing for non-deterministic model outputs, and hallucination monitoring.

Programme Governance & Financial Rigour

• P&L & Commercial Accountability: Take full financial responsibility for large-scale engineering programmes (£10M+ annual revenue), optimising pod structures and delivery velocity to protect and grow margins.
• AI Risk & Responsible Governance: Proactively identify systemic delivery and technical risks—including model drift, IP leakage, licensing issues in generated code, data privacy breaches, and security vulnerabilities (OWASP Top 10 for LLMs).
• Regulatory & Safety Compliance: Ensure all delivery frameworks adhere strictly to evolving AI governance standards, copyright guidelines, data privacy regulations (GDPR), and regulatory frameworks (e.g., EU AI Act).

Global Leadership & Stakeholder Management

• Distributed Team Orchestration: Direct and empower distributed cross-functional teams (Software Engineers, AI/ML Engineers, Prompt Engineers, DevOps/LLMOps Specialists, QA Engineers) of 100+ people across global delivery centres.
• CTO & Engineering Leadership Advisory: Serve as the primary delivery escalation point for client engineering executives. Translate complex AI capabilities and metrics (cycle time, lead time, deployment frequency, model accuracy) into clear business outcomes.
• Engineering Culture & Upskilling: Drive continuous learning across delivery teams, championing modern software engineering standards, AI-driven development patterns, and prompt engineering best practices.