AI Engineer

Our founders have built energy flex markets, led transactions of billion-dollar businesses, and built software agencies that consistently deliver engineers in the top 3% globally. That pedigree shapes how we think, how we work, and what we expect from the people who join us.

The role

Most AI engineering jobs are demos and pilots. Ours are production systems: agent workforces running rostering, claiming, payroll and compliance workflows end to end, and orchestration layers making operational decisions around the clock in regulated industries. Not chatbots bolted onto a UI. Autonomous systems with audit logs, bounded autonomy and escalation paths, because that's what the work demands.

You'll build agentic orchestration from the ground up: the harness, the tooling, the evaluation, and the deterministic fallbacks that make agents safe to run in production. You'll decide when a process should be handled by an agent and when it should fall back to code, and you'll own that decision and its consequences.

What you will do

• Design and build agentic orchestration layers: agent harnesses, tool interfaces, memory and state management, escalation paths

• Integrate and evaluate LLMs against real operational workloads, tested against historical data and simulation before production

• Build the deterministic guardrails: bounded autonomy, audit logging and code-based fallbacks where agents can't yet be trusted

• Apply classical ML (random forests, gradient-boosted models) for decision-making and scoring where it beats generative approaches

• Turn messy operational processes into agent-runnable workflows, working directly with founders and clients

• Help set the standard for how we build, test and ship agentic systems

What you bring — Required

• Deep Python expertise and a track record of building complex systems from scratch, not configuring platforms

• Practical experience building agentic workflows or orchestration layers, including LLM integration and evaluation

• Judgement about when to push agentic and when to fall back on deterministic code for reliability

• A rigorous approach to testing non-deterministic systems: evals, simulation, historical replay

• Clear communication and full ownership without micromanagement

Strongly preferred

• Classical ML in production decision systems

• Deploying AI into regulated or compliance-heavy environments

• Deep API integration experience: ERPs, payroll, billing, market systems

• Flex market and energy systems experience is always a big bonus

Nice to have

• Contributions to open-source agent frameworks or eval tooling

• Exposure to field-service or logistics operations

Why this role

The systems you build here will be running real businesses within weeks of you joining, alongside people who've genuinely done it before. If you're tired of AI theatre and want your work measured by operations it runs, this is the role.

What you get

• A competitive package that reflects the calibre we hire at

• Access to every AI tool on the market (as long as it has passed security) with team support and budget to get you set up and moving fast

• Hard, interesting problems: real AI in production, real scale, real consequences. Not demos and decks

• Direct access to some of the most experienced operators in the country

• A team that will stretch you, back you, and make you better

The interview process

Our process is straightforward and moves fast: a short video interview (we know engineers hate this — the only people who don't hate it are in sales — but it's an important and necessary step, and if you can't push outside your comfort zone, you're not going to fit in here), then a technical deep-dive on real problems, not puzzles, then a final conversation with the founders. We move quickly for the right person.