Senior AI Engineer – Harness Engineering (Kimchi)

Kimchi is the AI platform inside CAST AI. We started by helping companies run LLMs on their own Kubernetes clusters – now we’re building the execution layer where agents do real work.

Our Infrastructure today: multi-model inference (MiniMax, Kimi, GLM-5, Nemotron, Deep

Seek) with intelligent routing, an OpenAI-compatible API, and deployment flexibility from our GPUs to your VPC. The inference layer is the foundation. What we’re hiring for sits on top of it: coding agents, agent runtimes, orchestration systems, and the reliability engineering that makes them actually finish things.

Tech Stack: TypeScript, Go, Kubernetes, AWS/GCP/Azure, MCP, Prometheus/Grafana/Loki, GitLab CI, ArgoCD.

Why harness engineering matters here OpenAI and Anthropic ship models. They also ship one harness each – the scaffolding that turns a raw model into something that can plan, execute, recover, and complete work. We ship a different kind of harness: one built for cost-conscious, long-horizon autonomy, running on inference infrastructure we control end-to-end. A decent model with a great harness beats a great model with a bad harness. We’ve watched this play out. The gap between what today’s models can do and what you see them doing is largely a harness gap – and that gap is where we operate.

What you’ll build

Our harness is built around spec-driven autonomy: meta-prompting, fresh context per task, worktree-per-slice git strategy, automatic replanning, crash recovery, stuck detection. We’re implementing Ralph loops – when the model tries to exit, we intercept and reinject the goal into a fresh context. The agent reads state from disk and continues. Multi-session, multi-day work, without context rot.

The harness surface.

CLI, TUI, MCP integration, sandboxed execution, telemetry. Our AGENTS.md is short – every line traces to a specific thing that went wrong. TypeScript on the surface, Go where it matters.

What success looks like (after 6 months):

• You’ve shipped at least one major harness feature end-to-end: designed it, built it, measured it, iterated.
• You’ve added constraints to our AGENTS.md based on failures you personally observed and diagnosed.
• You’ve improved a measurable reliability metric – completion rate, context efficiency, or cost per successful task.
• You’ve formed strong opinions about where our harness is load-bearing and where it’s dead weight.

What makes this different (with receipts) You’ve seen the pitch: “we route to the best model.” Everyone says that. Here’s what we actually have:

• GPU infrastructure we own. Not just an API reseller. From GPU placement across clouds to the inference endpoint your agent calls – we control the cost curve.
• A harness-first thesis. We treat agent failures as configuration problems, not model problems. When we moved from a stock harness to our own, completion rates on internal benchmarks improved by 40%+ on the same model.
• Agents.md that earns every line. No brainstormed rules – every constraint in our system prompt traces to a real failure we saw and fixed.

What’s in it for you?

• Competitive salary (€6,500 – €9,000 gross, depending on the level of experience).
• Enjoy a flexible, remote-first global environment.
• Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology
• Equity options.
• Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks.
• Spend 10% of your work time on personal projects or self-improvement.
• Learning budget for professional and personal development – including access to international conferences and courses that elevate your skills.
• Annual hackathon to spark new ideas and strengthen team bonds.
• Team-building budget and company events to connect with your colleagues.
• Equipment budget to ensure you have everything you need.
• Extra days off to help maintain a healthy work-life balance.