Senior Software Engineering

About the role

The Senior Manager, AI Engineering is accountable for delivering business outcomes through AI‑native engineering practices rather than traditional, human‑only software delivery models.

This role is similar in seniority and accountability to a Software Engineering Senior Manager but differs fundamentally in ways of working. Instead of running conventional engineering teams, the role orchestrates AI‑enabled SDLC, leverages AI agents and copilots (or equivalent) at every stage of delivery, and partners closely with the business to design and deliver AI‑enabled journeys.

The role combines engineering leadership, AI fluency, product thinking, and business transformation, ensuring AI is used safely, effectively, and at scale to accelerate value delivery.

What you’ll be doing

1. AI‑Native Engineering Leadership
•    Own delivery outcomes using AI‑first SDLC models, embedding AI tools across requirements, design, build, test, deploy, and operate stages.
•    Shift delivery from capacity‑led engineering to outcome‑led, AI‑orchestrated execution.
•    Define and continuously evolve AI‑engineering ways of working, guardrails, and quality gates, including human‑in‑the‑loop and human‑gated approvals where required.

2. AI‑Enabled SDLC & Delivery Model
•    Champion the use of AI agents, copilots (or equivalent), and automation for:
•    Requirements discovery and refinement
•    Architecture and design generation
•    Code generation, review, and optimisation
•    Test case creation and execution
•    Release readiness, documentation, and operational insights
•    Ensure delivery models are compliant with security, risk, and governance expectations while maximising speed and quality.

3. Business Journey Enablement
•    Partner with senior business stakeholders to re‑imagine end‑to‑end journeys that are AI‑enabled by design, not retrofitted.
•    Translate business problems into AI‑solvable opportunities, shaping solution approaches that blend AI, platforms, and minimal custom engineering.
•    Act as a trusted advisor on where AI creates real value vs where traditional automation or engineering is sufficient.

4. Platform, Tooling & Ecosystem Ownership
•    Select, adopt, and govern AI engineering tooling (e.g., copilot, agent frameworks, prompt orchestration, evaluation frameworks).
•    Drive standardisation of AI delivery platforms to enable repeatability, reuse, and scale across teams and portfolios.
•    Collaborate with enterprise architecture, security, and risk teams to ensure safe adoption of AI technologies.

5. Talent, Capability & Culture
•    Build and lead small, high‑leverage AI‑enabled pods rather than large traditional engineering teams.
•    Upskill engineers to work effectively with AI as a delivery partner.
•    Foster a culture of experimentation, learning, and responsible AI usage.

6. Governance, Risk & Responsible AI
•    Ensure AI solutions adhere to organisational standards for ethics, security, data privacy, explainability, and auditability.
•    Embed quality, compliance, and risk controls directly into AI‑driven delivery pipelines rather than relying on downstream assurance.

What Makes This Role Different from a Traditional Software Engineering Senior Manager

Traditional Engineering Senior Manager

Senior Manager, AI Engineering

Manages large engineering teams

Orchestrates AI‑enabled delivery with smaller teams

Human‑led SDLC

AI‑augmented / AI‑orchestrated SDLC

Capacity and velocity focused

Outcome and value focused

Engineers write most code

AI generates, reviews, and optimises code

Business requirements handed over

Co‑creates AI‑enabled business journeys

Tooling as support

AI tools as core delivery engine

What you’ll need to succeed (Skills & Experience)

Essential

•    Proven experience as a Senior Engineering Manager or equivalent, delivering complex digital solutions.
•    Strong understanding of modern software engineering, cloud platforms (preferably AWS), APIs (REST), and DevOps.
•    Hands‑on exposure to AI‑assisted development tools (e.g., MS Copilot, Amazon Kiro, Claude Code, LLM‑based agents, automation frameworks).
•    Must have a solid technical background on Java or .NET or Python along with UI and backend technologies like React, Flutter, PostgreSQL, Neptune DB.
•    Experience partnering with business leaders to shape and deliver end‑to‑end digital journeys.
•    Strong leadership, influencing, and stakeholder management skills.

Desirable

•    Experience implementing AI‑enabled or agentic delivery models in enterprise environments.
•    Familiarity with Responsible AI, model governance, and risk controls.
•    Experience working in regulated or large‑scale enterprise contexts.
•    Background in product‑led or platform‑led delivery models.

Success Measures (What Good Looks Like)

•    Reduction in time‑to‑value through AI‑enabled delivery.
•    Measurable productivity uplift via AI‑assisted SDLC.
•    Business journeys redesigned with AI at the core.
•    High adoption and trust in AI‑enabled engineering practices.
•    Strong governance, quality, and risk outcomes with no compromise on compliance.