About the role
Problem Space
Logistics operations today are still largely:
- manual
- reactive
- fragmented across tools
- running on incomplete or late data
- full of conflicting constraints
- under real-time decision pressure
- driven by evolving business rules
- a mix of legacy and new systems
Much of this is unstructured: emails, documents, free-text updates, exceptions nobody modelled. That is where AI changes the game.
We’re building a system that
- ingests real-time operational data, structured and unstructured
- supports planning and execution decisions, with AI agents that act where it’s safe and hand over to humans where it isn’t
- adapts to constantly changing constraints
What You’ll Work On
- AI in production. Building LLM- and agent-powered features into production .NET services: tool calling, structured outputs, retrieval over operational data, document and message understanding.
- The seams. Designing the boundaries between deterministic business logic and probabilistic AI: validation, fallbacks, human-in-the-loop.
- Trust. Making AI measurable and trustworthy: evals, test sets, observability, guardrails and cost/latency budgets.
- Ownership. Owning features end to end, from problem framing with product to running them in production.
Design Principles
- keep things simple before scalable
- prefer explicit logic over magic abstractions, and that includes AI: deterministic where you can, model where you must
- optimize for change, not perfection (models, prompts and providers will change)
- measure AI behaviour, don’t trust vibes
- avoid “framework-driven architecture”
- accept that some parts will be ugly, temporarily
Tech Stack
.NET · Vue.js · service-oriented architecture · relational + operational data storage · cloud-based infrastructure · LLM APIs and agent tooling (e.g. Semantic Kernel / Microsoft.Extensions.AI, MCP) · vector/semantic search · eval and tracing tools
How We Build
- AI-native development is the default. You use coding agents (e.g. Claude Code, Copilot) every day.
- You own what you ship, whoever typed it: you review AI-generated code critically, test it and understand it.
What We Expect
- Strong, senior-level .NET engineering
- Ability to navigate uncertainty and work in ambiguity
- Willingness to challenge decisions
- Focus on outcomes, not just code
- Understanding of trade-offs and complex systems, including when not to use AI
- Preferring ownership over comfort
Strong Plus
- Having shipped LLM/AI features to production and kept them running
- Experience with evals, prompt/version management or AI observability
- Python for prototyping and data work
- Logistics or other real-time operations domain experience
What You Won’t Find Here
- over-engineering everything upfront
- unnecessary microservices
- “clean architecture” for the sake of it
- process-heavy development
- AI demos that never reach production
- wrapping a chatbot around a problem and calling it solved