AI Engineer – Agentic Systems

5+ years of experience

Ukraine, Europe

Fulltime, Remote

About the company

Our client is an AI technology company developing an intelligent inventory planning platform that combines forecasting, optimization and execution in one product. The platform is designed for customers across wholesale, retail, manufacturing and food service. At its core is a proprietary time series foundation model trained on millions of demand series and billions of data points across industries. The company is expanding its engineering team and is looking for an AI Engineer to build the next generation of agentic capabilities around its forecasting and optimization technology.

About the role

You will build the AI agent that customers will interact with every day.
Your role will be to connect language models with existing forecasting and optimization capabilities and build the software that makes the agent useful, reliable and safe.
As an early engineering hire, you will work closely with the founding team and take ownership of the agentic system, from its execution loop and MCP tools through evaluation and production.
You will help define how the agent uses context, which models it should use, when it should take action and when it should ask for confirmation. Feedback from real customer tasks will directly influence what you build next.

What you’ll work on:

Agent Harness

Build the runtime around the model, including planning and tool-use loops
Manage session state, context and memory
Implement retries, recovery and resumable multi-step tasks
Provide customers with clear progress, results and opportunities to intervene

MCP and Tools

Build Model Context Protocol (MCP) servers and integrations
Expose customer data, forecasts and planning actions to AI agents
Design clear tool schemas with validated inputs and outputs
Implement access controls and confirmation flows for changes to customer data

Context and Grounding

Provide the agent with relevant customer data, business rules and conversation history
Use retrieval and context management to keep responses traceable and useful as tasks become more complex
Keep calculations and business-critical logic in tested tools rather than relying on the model

Evaluations

Turn real customer tasks and failure cases into repeatable evaluation tests
Measure task completion, tool usage and correctness
Combine automated checks, human review and model-based graders
Identify regressions before shipping changes to prompts, tools or models

Model Selection

Evaluate frontier models and open-weight alternatives against real workloads
Measure reliability, latency and cost per completed task
Evaluate the impact of retries and hosting overhead
Make informed decisions about model routing, caching and self-hosting

Production Reliability

Trace agent runs and investigate failures across models, tools and data
Test permission boundaries, prompt injection and unintended repeated actions
Own production rollout, monitoring and continuous improvements
Work closely with product and customer-facing engineering teams

Tech Stack

Python
FastAPI
PostgreSQL
Polars
LangGraph
LLM APIs
MCP (Model Context Protocol)
Claude Code

What we’re looking for

Strong Python and backend engineering skills
Experience building tested and maintainable backend services
Good understanding of APIs, asynchronous execution and persistent state
Hands-on experience building and shipping LLM-powered applications
Experience with LLM tool use and structured outputs
Ability to improve AI-powered applications based on real user behavior and feedback
Understanding of agentic architectures, tool calling and multi-step AI workflows
Strong problem-solving and ownership mindset
Comfortable working closely with founders and taking ownership of technical decisions
Professional English or German; either language is sufficient

Nice to have

Experience with LangGraph or similar agent orchestration frameworks
Experience building MCP servers or MCP-based integrations
Experience with evaluation frameworks for LLM/agentic systems
Experience with RAG, retrieval and context management
Experience with AI observability and tracing
Experience with prompt injection and AI application security
Experience evaluating and routing different LLMs
Experience with production AI systems and monitoring

How we work

We work in short development loops: identify a customer task, build a solution, evaluate it and ship. You will discuss technical decisions directly with the founding team, share failures early and help establish engineering and evaluation practices as the AI team grows.

What we offer

Fully remote position
Opportunity to work on an AI product combining LLMs, forecasting and optimization
High level of ownership and direct impact on the product
Close collaboration with the founding team
Opportunity to shape the architecture and engineering practices of an early-stage AI product
Work with modern AI technologies, including LLMs, agentic systems and MCP