Agentic AI Engineer
About The Position
Catapult is building the future of sports performance technology. Since 2006, we’ve helped more than 5,000 teams use data, science and technology to improve athlete health, readiness and performance. Our customers include teams across the NFL, NBA, NHL, MLS, EPL, AFL, NRL, NCAA and many more. Now we're building the next layer of that platform: AI that can reason across everything we know about an athlete and turn it into intelligence a coach or performance practitioner can trust. We're looking for an Agentic AI Engineer who has already shipped production AI systems and understands what it takes to make them reliable, measurable and trustworthy.
Requirements
- Personally shipped a production agentic AI system used by real users.
- Hands-on experience with memory or persistent state.
- Hands-on experience with tool use or tool calling.
- Hands-on experience with multi-step reasoning or workflows.
- Hands-on experience with production deployment and operation.
- Built or substantially contributed to a production multi-agent system.
- Understand agent routing and orchestration.
- Understand specialist agent composition.
- Understand dependency-aware workflows.
- Understand parallel and sequential execution.
- Understand conflicting agent outputs.
- Understand response synthesis.
- Hands-on experience calibrating probabilistic ML or AI systems.
- Comfortable with Platt scaling.
- Comfortable with Isotonic regression.
- Comfortable with Expected Calibration Error (ECE).
- Comfortable with reliability and calibration curves.
- Comfortable with confidence and uncertainty estimation.
- 5+ years of professional experience in applied ML, AI or software engineering.
- Strong Python.
- Strong software engineering fundamentals.
- Experience building and operating production systems.
Nice To Haves
- Experience with LangGraph, AutoGen, CrewAI or equivalent frameworks.
- Experience with production RAG and reranking.
- Foundation-model fine-tuning or domain adaptation.
- LoRA, PEFT or similar techniques.
- LLM observability and drift detection.
- Evaluation harnesses and automated regression testing.
- Human-in-the-loop architectures.
- Confidence thresholds and escalation models.
- Causal or counterfactual reasoning.
- Go/Golang.
- AWS, including ECS, EC2, Lambda, SNS or SQS.
- GraphQL, REST or gRPC.
- PostgreSQL or MongoDB.
- Experience working with sport scientists, clinicians or other domain experts.
- Familiarity with workload, readiness, recovery, biomechanics or athlete performance data.
Responsibilities
- Design and ship specialist AI agents that use memory, tools, data and multi-step reasoning.
- Build multi-agent orchestration that routes work between specialist agents, manages dependencies and synthesises conflicting outputs.
- Develop systems that evaluate confidence, uncertainty and consequence before recommendations reach a practitioner.
- Build human-in-the-loop escalation so the system knows when to answer, when to ask for more information and when to defer to a human.
- Create workflows that turn sport scientist expertise into validated, versioned and testable agent capabilities.
- Build evaluation, observability and regression testing so agent performance can be measured and improved in production.
- Work with domain experts to ensure AI outputs are grounded, traceable and actionable.
Benefits
- Health insurance
- Dental insurance
- Vision insurance
- 401(k) retirement plan with company match
- Generous paid leave
- Recognized company holidays
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
