Agentic AI Engineer, 3Commas

• 5+ years of backend or ML engineering, including at least one year building LLM-based systems in production. You understand agent failures one level below the framework and can fix them in your own code
• A track record of eval-driven development: you can show a quality metric you kept honest over time and how you used it to improve an agent
• Strong Python and production fundamentals (services, queues, streaming, observability, Kubernetes). You're comfortable following a bug into TypeScript or Go, and you treat pipelines, migrations and guards as real engineering
• Application of statistical principles on backtesting methodology
• A product mindset: you judge your work by what the user gets, and you take ambiguous goals to shipped, measured results
• Working proficiency in English (B2+)

Desirable

• You will join the Applied AI team, a small product team focused entirely on the engine behind QuantPilot's agent: AI research, strategy generation and the agent platform that runs them
• The central problem of this role is making agents produce strategies that are statistically sound, not only plausible-looking, and proving it with evaluation
• We judge our work by what traders get out of it, not by features shipped
• Models change every few months, so we invest in the engineering around them and prefer to own that code rather than depend on heavy frameworks
• Own the agent architecture end to end: orchestration, tool loops, context management, sandboxes and model routing, including the quality, cost and latency trade-offs of running it in production
• Own the contracts between the engine and the product, including how the frontend renders agent events
• Build and own the evaluation layer: offline evals, backtest-based quality metrics and regression detection for every prompt or model change
• Bring statistical rigor to strategy evaluation: overfitting detection, robustness checks, and walk-forward and out-of-sample validation
• Build the data foundations the agent reasons over, with provenance, validation and a consistent model of market entities, and make the system observable enough to diagnose non-deterministic failures in production
• Shape what gets built: refine requirements with product, set technical direction through RFCs and ADRs, and mentor engineers moving into LLM systems