Engineering Manager

We are looking for an Engineering Manager on a contract basis for a leading buy side Investment client in London.

The Engineering Manager will lead a critical transition from external delivery dependency to in-house engineering capability — with a unique twist: you'll reshape how the entire team delivers software using AI-augmented engineering practices.

This is a principal engineer who manages, not a manager who codes occasionally. You'll carry real individual contribution (code reviews, architecture decisions, hands-on delivery on complex workstreams) alongside leading a small, high-performing in-house squad and stewarding relationships with external resources.

WHAT YOU'LL OWN

Technical Leadership & Strategy

• Architecture ownership across three technical domains: AI platform, proprietary .NET applications (core business systems), and data/analytics/reporting infrastructure
• Individual contribution on complex technical workstreams — code reviews, architecture decisions, hands-on delivery alongside your team
• Set technical standards, code-quality bar, and delivery cadence for in-house engineering
• Define the AI-augmented delivery model: how your team uses agentic development tooling, spec-driven approaches, and LLM-assisted pipelines to punch well above its weight

Team Leadership

Direct leadership of small, senior in-house engineering squad

• Set hiring strategy, onboarding, growth, and technical mentorship
• Foster culture of technical excellence, ownership, and continuous learning
• Manage performance, development, and retention of core team

External Resource Management

Steward relationships with external development partner during knowledge-transfer phase

• Transition workload progressively from external to in-house delivery
• Maintain quality and momentum while shifting capability in-house
• Define SLAs, delivery expectations, and handoff protocols

Knowledge Absorption & Transition

• Reverse-engineer understanding of proprietary systems, architectural patterns, and business domain knowledge from external partner
• Document critical systems, business logic, and technical decisions
• Build durable, maintainable in-house codebase that doesn't perpetuate external partner's patterns
• Plan and execute phased transition from external dependency to self-sufficient in-house delivery

AI-Augmented Delivery Model

• Design and implement agentic development practices: code generation, testing, documentation automation
• Evaluate Claude Code, agentic pipelines, and spec-driven approaches for your specific context
• Build evaluation frameworks and quality gates for AI-assisted delivery
• Create proof-of-concepts showing how AI tooling reduces routine work and frees senior engineers for high-value problems
• Progressively shift external partner's routine work to AI-assisted in-house delivery

WHAT WE'RE LOOKING FOR

You are a principal engineer first, manager second.

• 2 plus years of people leadership experience — enough to have handled the hard moments (difficult conversations, performance management, team conflicts), but recent enough that your instincts are still primarily technical
• Strong technical credibility — engineers trust your judgment because you know the work
• Individual contribution mindset — you code, review, architect, and deliver alongside your team, not from the sidelines
• Hands-on delivery track record — comfortable owning complex workstreams end-to-end

Technical Requirements:

Credible across three domains (deep expertise in one or two sufficient):

• AI Platform & LLM Integration — Building with Claude, OpenAI, agentic systems, RAG, or similar
• Application Engineering (.NET) — Hands-on experience with C#/.NET, microservices, cloud deployment, or application modernization
• Data/Analytics/Reporting — SQL, ETL/ELT, analytics platforms, or data pipeline architecture

Required:

• Python or TypeScript (primary languages for AI-augmented delivery)
• SQL and relational databases
• Cloud platforms (Azure, AWS, or GCP)
• API design and integration patterns
• Experience shipping production systems in financial services preferred

AI-Augmented Delivery Vision

• Clear perspective on agentic development tooling — not just hype, but genuine experience with how Claude Code, code generation, and AI-assisted testing actually work in practice
• Understanding of spec-driven approaches — ability to write executable specifications that feed AI-assisted pipelines
• Track record with small, high-performing teams — you've made a small group punch above its weight
• Build-over-buy mindset — bias toward in-house ownership of critical capability
• Practical about AI — knows where AI tooling helps (boilerplate, testing, documentation) and where human judgment still matters (architecture, edge cases, security)