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
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