Management Level
D
Job Title: AI Enabled Engineering Leader
Reports To: Head of Engineering
Experience: 15+ years in Software Engineering
Role Overview
This is a senior, hands-on engineering leadership role reporting directly to the Head of Engineering. The role defines, implements, and scales AI‑enabled engineering practices that materially improve developer experience, delivery speed, and software quality across Scrum teams.
The role blends technical leadership, strategy, execution, and influence. You will work directly with teams to embed AI across the full SDLC-requirements, design, coding, testing, review, non‑functional requirements, and CI/CD-while establishing guardrails, standards, and a sustainable AI Community of Practice (CoP).
This is not an advisory role. You will build, pilot, coach, and scale.
Key Responsibilities
1. Strategic Partner to the Head of Engineering
- Act as a trusted advisor on AI adoption, developer experience, and delivery effectiveness
- Shape and execute engineering strategy for AI‑enabled delivery
- Translate strategy into practical, team‑level adoption
2. Hands‑On Enablement with Scrum Teams
- Work directly with teams to embed AI into day‑to‑day delivery
- Pair with engineers on real work: requirements, design, coding, testing, PRs, and releases
- Identify friction points and continuously improve tooling and practices
3. AI‑Enabled SDLC (End‑to‑End)
- Define and operationalize AI usage across: requirements, development, testing, review, NFRs, and CI/CD
- Establish clear, practical “how we build software here” standards
4. Best Practices, Standards & Guardrails
- Define responsible AI usage standards: validation, testing, security, documentation, traceability
- Produce lightweight standards, templates, prompt patterns, examples, and checklists teams use
5. Developer Experience & Tooling
- Embed AI tools into IDEs, PR workflows, testing, and CI/CD pipelines
- Improve the developer inner loop: faster feedback, reliable pipelines, reduced toil
- Build POCs, reference implementations, and reusable templates for GenAI and agent‑based systems
- Support teams moving from experimentation to production
6. Community of Practice (CoP) Leadership
- Create and lead an AI Engineering CoP
- Establish playbooks, libraries, demos, office hours, and team champions
- Evangelize practical GenAI and agentic AI usage
- Run workshops, demos, brown‑bags, and internal documentation
- Enable safe, pragmatic adoption without disrupting delivery
7. Measurement & Continuous Improvement
- Define and track metrics: cycle time, lead time, deployment frequency, defect escape rate, test effectiveness, CI/CD health, developer satisfaction
- Run pilots, measure outcomes, and scale what works
Required Qualifications
- 15+ years in software engineering with strong hands‑on coding background
- 5+ years leading AI, productivity, platform, or enablement initiatives
- Recent (2–3 years) experience scaling GenAI‑assisted SDLC adoption
- Experience leading initiatives across multiple teams
- Deep understanding of SDLC, CI/CD, and quality engineering
- Strong influence, coaching, communication, and executive‑level presence
Coding Languages & Frameworks
- Languages: Python, C#/.NET, JavaScript/TypeScript (Node.js), SQL, Shell
- AI / GenAI: PyTorch, TensorFlow, Hugging Face, LangChain, LangGraph, LlamaIndex, Vector DBs
- Agentic Systems: LLM agents, tool use, planning, memory, reflection, RAG, prompt engineering, guardrails
- Architecture: Agent orchestration, event‑driven systems, human‑in‑the‑loop workflows
- Testing & Quality: Agent testing, probabilistic vs deterministic testing, regression, simulation
- AI Ops: Monitoring, drift, cost, CI/CD for AI systems, safe rollout and rollback
- Cloud: AWS or Azure AI services
Preferred Qualifications
- Experience improving developer productivity at scale
- Modern CI/CD and test automation modernization
- Building and sustaining Communities of Practice
- Exposure to security, reliability, and observability standards
What Success Looks Like (6–12 Months)
- AI‑enabled practices adopted across most Scrum teams
- Measurable improvements in speed, quality, and predictability
- Reduced CI/CD and development friction
- A thriving, self‑sustaining AI CoP
- Clear executive visibility into outcomes
Why This Role Matters
This role ensures AI adoption is practical, responsible, and impactful shaping how software is built and improving outcomes for engineers, the business, and customers.
We are committed to equality of opportunity for all staff and applications from individuals are encouraged regardless of age, disability, sex, gender reassignment, sexual orientation, pregnancy and maternity, race, religion or belief and marriage and civil partnerships. Please note any offer of employment is subject to satisfactory pre-employment screening checks.
