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