AI-Enabled Engineering Leader

AI‑Enabled Engineering Leader

Management Level

D

Equiniti provides comprehensive solutions to support organizations throughout their corporate lifecycle, including managing shareholder engagement, maintaining stock registers, facilitating ownership transfers, enabling shareholder meetings, paying dividends, supporting IPOs, and administering employee equity plans. We are dedicated to revolutionizing shareholder services by developing digital solutions that facilitate dematerialization for issuers and shareholders. 

EQ is a fintech that connects the future of capital, communications, and governance, building trust and confidence in every market we serve. Our values set the core foundations to our success. We are TRUSTED to deliver on our commitments, COMMERCIAL in building long term value, COLLABORATIVE in our approach and we IMPROVE by continually enhancing our skills and services. There has never been a better time to join EQ.

Job Title: AI‑Enabled Engineering Leader

Reports To: Head of Engineering 

Experience: 15+ years of experience in Software Engineering with strong hands-on coding

Role Overview 

This role is a senior, hands‑on engineering leadership position reporting directly to the Head of Engineering. The leader will define, implement, and scale AI‑enabled engineering practices that materially improve developer experience, delivery speed, and software quality across Scrum teams. 

The role combines technical leadership, strategy, execution, and influence. You will work directly with Scrum teams to adopt AI across the SDLC—requirements, design, coding, testing, code review, non‑functional requirements, and CI/CD—while establishing best practices, guardrails, and a sustainable 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 engineering leader and advisor to the Head of Engineering on AI adoption, developer experience, and delivery effectiveness 

• Shape the engineering strategy for AI‑enabled software delivery 

• Translate strategy into executable plans adopted by Scrum teams 

2. Hands‑On Enablement with Scrum Teams 

• Work directly with Scrum teams to embed AI into day‑to‑day delivery 
• Requirements clarification and acceptance criteria 
• Design and technical discovery 
• Code generation and refactoring 
• Unit, integration, and functional test creation 
• Pull request reviews and release readiness 
• Identify friction points and continuously improve practices and tooling 

3. AI‑Enabled SDLC (End‑to‑End Ownership) 

• Define and operationalize AI usage across the full SDLC:  
• Requirements & design 
• Development & refactoring 
• Testing (unit, functional, integration) 
• Code review and quality gates 
• Non‑functional requirements (security, performance, reliability, observability) 
• CI/CD and release automation 

4. Best Practices, Standards & Guardrails 

• Establish best practices for responsible AI usage:  

• Validation and review of AI‑generated code 

• Test and security expectations 

• Documentation and traceability 

• Define lightweight standards that enable speed rather than constrain it 

• Produce templates, examples, prompt patterns, and checklists teams actually use 

5. Developer Experience & Tooling 

• Integrate AI tools seamlessly into the developer workflow:  

• IDEs 

• Code reviews and PR automation 

• Testing frameworks 

• CI/CD pipelines 

• Build reference implementations and POCs for agent-based GenAI systems 

• Support engineering teams moving from experimentation to production 

• Create reusable templates, libraries, and example repositories 

6. Community of Practice (CoP) Leadership 

• Create and lead an AI Engineering Community of Practice 

• Build a sustainable model including:  

• Playbooks and shared libraries 

• Ensure practices evolve as tools and needs change 

• Evangelize practical usage of GenAI and agentic AI systems across engineering teams 

• Educate engineers on LLM-powered agents, tool-using agents, and human-in-the-loop workflows 

• Run workshops, demos, brown-bag sessions, and internal documentation 

• Help teams adopt AI safely and pragmatically without disrupting delivery 

7. Measurement & Continuous Improvement 

• Define success metrics aligned with engineering and business outcomes:  

• Cycle time and lead time 

• Deployment frequency, and defect escape rate 
• Test effectiveness and CI/CD health 

• Run pilots, measure results, and scale what works 

Required Qualifications 

• 5+ years’ experience in AI, leading engineering productivity, platform, or enablement initiatives
• Strong focus in last 2 to 3 years focused on scaling GenAI‑assisted SDLC adoption

• Deep understanding of SDLC, CI/CD, and quality engineering practices 

• Proven ability to drive adoption through influence and coaching 

• Exposure to security, reliability, and observability standards in production systems 

What Success Looks Like (6–12 Months) 

• AI‑enabled engineering practices adopted by most Scrum teams 

• Measurable improvements in delivery speed, quality, and predictability 

• Reduced friction in development and CI/CD workflows 

• Clear executive visibility into engineering improvements and outcomes

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.

Experience Level

Senior Level