Agentic AI Engineer

8 days ago

Responsibilities

Design and ship production specialist AI agents using memory, tools, data, and multi-step reasoning.
Build multi-agent orchestration for routing, dependency-aware workflows, parallel and sequential execution, and response synthesis.
Develop systems that evaluate confidence, uncertainty, and consequences before recommendations reach practitioners.
Create human-in-the-loop escalation workflows that determine when to answer, request more information, or defer to a human.
Translate sport scientist expertise into validated, versioned, and testable agent capabilities.
Build evaluation, observability, regression testing, and drift-detection systems for measuring and improving agent performance in production.
Collaborate with domain experts to ensure AI outputs are grounded, traceable, actionable, and trustworthy.

Personally shipped a production agentic AI system used by real users, including memory or persistent state, tool use or tool calling, multi-step reasoning or workflows, and production deployment and operation.
Built or substantially contributed to a production multi-agent system and understand agent routing, specialist composition, dependency-aware workflows, execution patterns, conflicting outputs, and response synthesis.
Hands-on experience calibrating probabilistic ML or AI systems using Platt scaling, isotonic regression, Expected Calibration Error, reliability curves, and uncertainty estimation.
At least 5 years of professional experience in applied ML, AI, or software engineering.
Strong Python and software engineering fundamentals, with experience building and operating production systems.
Experience with production RAG and reranking, foundation-model fine-tuning or domain adaptation, LoRA, PEFT, LLM observability, evaluation harnesses, automated regression testing, human-in-the-loop architectures, confidence thresholds, escalation models, or causal and counterfactual reasoning.
Experience with Go, AWS, GraphQL, gRPC, PostgreSQL, or MongoDB is valued.
Experience with LangGraph, AutoGen, CrewAI, or equivalent frameworks is valuable.
Experience working with sport scientists, clinicians, or other domain experts is a plus; familiarity with workload, readiness, recovery, biomechanics, or athlete performance data is helpful.

Target total compensation is $107,250–$214,500 per year, inclusive of base salary and a target incentive plan.
Generous paid leave and recognized company holidays.
Health, dental, and vision insurance.
401(k) retirement plan with company match.

501-1,000 employees