Job Description

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

Responsibilities:
  • 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.
Required Qualifications:
  • 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
Nice To Have:
  • 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
Skills:
  • AI-native development
  • Problem framing
  • Testing
  • Code review
Technologies:
  • .NET
  • Vue.js
  • LLM APIs
  • Semantic Kernel
  • Microsoft.Extensions.AI
  • MCP
  • Python
Note:

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