Lead AI Engineer - Perm - Manchester is a AI Engineer role (full-time). with SRG. in MANCHESTER, GB. Compensation shown: $135K–$149K (est.). Imported listing (source: artificialintelligencejobs.co.uk). Apply on the employer's site (artificialintelligencejobs.co.uk).
Imported listing (source: artificialintelligencejobs.co.uk) · Apply on artificialintelligencejobs.co.uk
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Imported job description
Sourced from artificialintelligencejobs.co.uk
A leading digital and retail business in the global betting and gaming industry seeks a hands-on Lead AI Engineer to improve developer productivity using LLMs and coding agents. You will build tooling and workflows that work in real codebases, measure results honestly, and help engineers adopt AI effectively. This is not a model-building or research role; it focuses on practical application and evidence-based evaluation.
Responsibilities
Identify where AI helps engineers and where it does not, and build tooling and workflows that make it work in real codebases.
Use the best available models, coding agents, and open-source tools to improve software delivery.
Measure the results of AI-assisted development honestly and provide evidence of impact.
Integrate AI tools with Git platforms, CI/CD, issue trackers, and observability tooling.
Design evaluations for AI outputs and apply experimental rigour.
Influence engineers and win over sceptics without line authority.
Requirements
Strong professional software engineering experience, including building and operating production software.
Excellent programming and software design skills, with a solid grasp of testing, CI/CD, and code review.
SRG is a specialist life-sciences talent and recruitment company. It connects organisations with life-sciences professionals and provides tailored workforce solutions supporting hiring, careers, and long-term talent partnerships.
Hands-on experience building with LLM APIs, including tool calling and agentic workflows.
Day-to-day use of AI coding tools and agents on real codebases, with an evidence-based view of where they help and where they hurt.
A practical understanding of how LLMs and agents fail: plausible but wrong code, fixes that mask symptoms, tests gamed to pass, context limits, and cost growth.
Experience integrating with Git platforms, CI/CD, issue trackers, and observability tooling.
Experience designing evaluations for AI outputs, or comparable experimental rigour.
Python (or similar) and SQL for preparing code, documents, and tickets for retrieval.
Credibility with engineers and the ability to influence without line authority, including winning over sceptics.
Nice to Have
Bitbucket
Jenkins/Harness
Docker
Kubernetes
AWS
FastAPI
MCP
DORA-style measurement
RAG and vector search
Self-hosted open-weight models
Experience in a regulated environment such as gambling or financial services