Agentic AI Engineer

Henderson Scott; Greater London; Remote; GBP 90,000 - 130,000; Full time

Job summary

Henderson Scott is seeking an experienced Agentic AI Engineer for a 12-month contract to drive adoption of AI engineering practices in large-scale delivery environments. You will champion hands-on agentic workflows, mentor teams, and establish enterprise standards for AI-assisted software development across the SDLC.

This remote (UK-based) role requires hands-on AI engineering, collaboration across multiple teams, and an active SC clearance.

Qualifications

• Hands-on AI engineering experience building agentic workflows.
• SDLC AI application across testing, code review, documentation and legacy modernisation.
• Direct experience with modern AI coding assistants and agent tooling.
• Culture & governance: defining cross-team engineering standards and adoption.
• Collaboration & leadership: influencing without formal authority, coaching and champions networks.
• Security clearance: active SC clearance must be held.

Responsibilities

• Build and run agent workflows, handle model selection, prompt/harness engineering, agent-tool integration.
• Apply AI across the full software lifecycle, including test generation and documentation.
• Leverage and integrate AI coding assistants and agent tooling (Claude Code, GitHub Copilot, Codex, Kiro).
• Define standards, guardrails and engineering practices across teams with adoption evidence.
• Influence without formal authority across org boundaries, engaging with providers and suppliers.
• Coach and run champions networks to build a self-sustaining community of practice.

Job description

Agentic AI Engineer / AI Adoption Specialist

• Location: Remote (UK-based)
• Contract Length: 12 Months
• Rate: Competitive (Inside IR35)
• Security Clearance: Active SC ESSENTIAL

Job Description We are looking for an experienced Agentic AI Engineer for an initial 12-month contract to drive the adoption and implementation of cutting-edge AI engineering practices across large-scale delivery environments. In this role, you will champion hands‑on agentic AI workflows, mentor engineering teams, and establish enterprise standards for AI‑assisted software development, spanning the entire Software Development Life Cycle (SDLC).

Key Responsibilities

• Agentic AI Engineering: Build and run agent workflows, handle model selection, prompt/harness engineering, agent-tool integration, and manage reusable skills and instructions.
• SDLC Integration: Apply AI across the full software lifecycle, including test generation, code review augmentation, documentation, legacy comprehension, and modernisation.
• AI Tooling & Ecosystem: Leverage and integrate AI coding and agent tooling such as Claude Code, GitHub Copilot, Codex, Kiro, and MCP server development.
• Standards & Governance: Define standards, guardrails, and engineering practices adopted across multiple teams, with demonstrable evidence of successful adoption.
• Stakeholder Influence: Influence without formal authority across organisational and team boundaries, engaging effectively with technology providers and suppliers.
• Community & Coaching: Provide targeted training, coaching, and run champions networks to build a self‑sustaining community of practice.

Essential Skills & Criteria

• Hands‑on AI Engineering: Proven experience building agentic workflows, prompt/harness engineering, and reusable instructions.
• SDLC AI Application: Demonstrated success applying AI to testing, code review, documentation, and legacy system modernisation.
• Tool Exposure: Direct experience with modern AI coding assistants and agent tooling (e.g., Claude Code, GitHub Copilot, MCP servers).
• Culture & Governance: Track record of defining cross‑team engineering standards and driving wide‑scale adoption.
• Collaboration & Leadership: Strong influencing skills without formal authority, coupled with experience in coaching and running champions networks.
• Security Clearance: Must hold an active SC clearance.

Desirable Criteria

• Background in software engineering for large-scale production systems, sufficient to seamlessly embed within technical delivery teams.