Engineer, AI Engineer (Applied​/Software)

A well-capitalised private equity firm with long-duration capital and a portfolio of mid to upper-mid-market software companies. Rather than a single product team, you would work across the portfolio, embedded with the engineering organisations of multiple companies. The operating team you would join includes former CTOs, CEOs and senior operators, so there is real mentorship alongside the autonomy.

The opportunity

This is a forward-deployed role that blends hands-on engineering with strategic advisory. You will help portfolio companies become AI-native, turning promising ideas into production systems and leaving behind reusable playbooks as you go. One week you might architect an agentic system with a portfolio team, the next run a CTO roundtable on how to adopt it safely. It is a rare seat that is not locked into one codebase or one company, with a clear path toward senior technical leadership.

Responsibilities

• Advise portfolio CEOs, CTOs and engineering leaders on AI strategy, architecture and organisational design.
• Turn ambiguous, high-potential AI use cases into working production systems, embedding hands-on when needed.
• Assess AI readiness across data, systems and tooling, and pressure-test product ideas against real constraints.
• Coach engineers and leaders to raise team capability, leaving behind reusable patterns and playbooks.
• Spot repeatable patterns across the portfolio and run CTO round tables to spread the lessons fast.

Essential skills and experience

• 5 to 10 years in software engineering, backend or full stack.
• Time as a technical anchor (principal, architect or tech lead) who owned system-level decisions in production.
• At least one AI-native or AI-enabled product shipped end to end, with real ownership of architecture and rollout. Production, not a demo or prototype.
• Fluency in modern AI application architecture: LLM and agentic systems, RAG, retrieval and context engineering.
• Production AI reliability: evaluation pipelines, guardrails, structured outputs, model routing and fallbacks.
• The ability to earn trust and influence engineers, technical leaders and C-suite without formal authority.
• Willing to travel to portfolio companies roughly 6 days a month.
• BS in Computer Science or equivalent.

Nice to have

• Built a 0-to-1 product through the messy early phase.
• Consulting, advisory or multi-company experience (platform teams, internal tools orgs, or technical consulting).
• A track record of coaching or mentoring engineers and technical leaders.
• Background at an AI-native or forward-deployed engineering organisation, or a backend-strong software company.