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AI Engineer
3Commas.io. Own the agent architecture end to end, including orchestration, tool loops, context management, sandboxes, model routing, and production quality, cost, and latency trade-offs .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in backend and ML engineering with a focus on LLM-based systems, statistical evaluation, and production quality. Proven ability to mentor engineers and manage production incidents while ensuring observability and performance in high-load environments.
Highest-signal resume keywords
LLM-Based Systems DevelopmentPython ProgrammingStatistical Evaluation MethodologyKubernetes ManagementIncident Response Participation
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Backend EngineeringMachine Learning EngineeringStatistical PrinciplesEval-Driven DevelopmentHigh-Load SystemsLanguage Model Fine-TuningProduction Engineering FundamentalsTypeScript ProficiencyGo ProgrammingQuant Finance Background
Soft Skills
Product MindsetMentoring
Tools & Technologies
KubernetesMCP ProtocolKnowledge GraphsOntologiesSemantic Layers
Industry Keywords
FintechTradingObservabilityProduction QualityLatency Trade-Offs
Tech Stack
Tools & technologiesKubernetesPythonTypeScriptGo
About the role
Key responsibilities & impact- Own the agent architecture end to end, including orchestration, tool loops, context management, sandboxes, model routing, and production quality, cost, and latency trade-offs
- Own contracts between the engine and product, including frontend rendering of agent events
- Build and own offline evaluations, backtest-based quality metrics, and regression detection for prompt or model changes
- Apply statistical rigor to strategy evaluation, including overfitting detection, robustness checks, and walk-forward and out-of-sample validation
- Build data foundations with provenance, validation, and a consistent model of market entities
- Improve observability to diagnose non-deterministic production failures
- Refine requirements with product and set technical direction through RFCs and ADRs
- Mentor engineers moving into LLM systems
- Participate in on-call rotation, act as first responder for production incidents, and follow the incident response process
Requirements
What you’ll need- 5+ years of backend or ML engineering experience
- At least 1 year building LLM-based systems in production
- Track record of eval-driven development
- Strong Python and production engineering fundamentals, including services, queues, streaming, observability, and Kubernetes
- Ability to work with TypeScript or Go
- Experience applying statistical principles to backtesting methodology
- Product mindset and ability to take ambiguous goals to shipped, measured results
- Working proficiency in English (B2+)
- Quant finance background is a plus
- Experience with knowledge graphs, ontologies, semantic layers, or RAG is a plus
- Experience with Go is a plus
- Experience building software on the receiving end of the MCP protocol is a plus
- Experience with high-load, low-latency systems in fintech or trading is a plus
- Language model fine-tuning or training experience is a plus
- Participation in on-call rotation and incident response for owned services
Benefits
Comp & perks- Responsible use of AI-assisted development tools as part of the engineering workflow
- Participation in the team's on-call rotation with end-to-end service ownership
- Global/remote work arrangement