Senior Agentic AI Engineer
Customer location: USA
Candidates locations: EU
Language: English B2+
Position: Agentic AI Engineer
Level: Senior
Project 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.
Must-haves
•Production agentic AI
•Memory or persistent state
•Tool use or tool calling
•Multi-step reasoning or workflows
•Production deployment and operation
•Chatbots, prompt engineering and RAG alone are not enough.
•Multi-agent orchestration
•You have built or substantially contributed to a production multi-agent system.
•Agent routing and orchestration
•Specialist agent composition
•Dependency-aware workflows
•Parallel and sequential execution
•Conflicting agent outputs
•Response synthesis
•Experience with LangGraph, AutoGen, CrewAI or equivalent frameworks is valuable.
•Confidence calibration
•You have hands-on experience calibrating probabilistic ML or AI systems.
•Platt scaling
•Isotonic regression
•Expected Calibration Error (ECE)
•Reliability and calibration curves
•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
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 is a plus. You don't need to be a sports scientist, but familiarity with workload, readiness, recovery, biomechanics or athlete performance data.
