Senior Agentic AI Engineer

id 130140
Agentic AI Engineer
Senior
"Customer location: USA
Candidates locations: EU
Working hours - 11.00-20.00 CET (>50% overlap with New York)
Language: English B2+
Estimated start date: 16.11.2026
Duration: 12 months"
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 will help.