Senior AI Software Engineer (.NET)

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