Product Engineer: AI Agents & Production-Ready Apps

Bolter

United Kingdom

On-site

GBP 80,000 - 115,000

Full time

14 days+
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Job summary

Bolter is seeking a product engineer to own the full lifecycle of the product—from rapid prototyping to production—across the agent-building experience, the workspace, and the applications agents generate.

You will work directly with the GM in a lean team, shaping core technical decisions, balancing speed with reliability, and building a pipeline that turns plain-language descriptions into live, trustworthy agents.

Qualifications

  • Shipping production software experience, ideally at product-led startups.
  • Deep TypeScript across the full stack; Go experience a plus.
  • Experience with LLM apps, AI reliability, or real-time data infra.
  • Strong product instincts linking tech decisions to business impact.
  • Comfort prototyping quickly and shipping once viable.

Responsibilities

  • Own the technical roadmap across the three domains above, from research and prototyping through production.
  • Make architecture calls with real trade-offs - speed, reliability, cost, build vs buy, where AI does the work and where humans steer.
  • Ship working prototypes fast and harden what works. This is a validation-stage environment; polish comes after proof.
  • Use AI tools as a multiplier. We run lean, with a small human core augmented by a fleet of specialised AI agents for implementation.
  • Recruit and shape the early engineering team as we scale.
  • Stay close to the product. You'll work directly with the GM - expect to have strong opinions on UX, not just infrastructure.

Skills

TypeScript
Go
LLM design
AI reliability
Product instincts

Job description

Bolter is seeking a product engineer to own the full lifecycle of the product—from rapid prototyping to production—across the agent-building experience, the workspace, and the applications agents generate.

You will work directly with the GM in a lean team, shaping core technical decisions, balancing speed with reliability, and building a pipeline that turns plain-language descriptions into live, trustworthy agents.

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