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