Technical Support Engineer

Microsoft Microsoft · Big Tech · Fargo, ND +4 · Technical Support Engineering

This role involves designing and shipping scalable AI and software systems, including an agent platform, production engineering rails for AI, language model pipelines (training, fine-tuning, RLHF, automated evaluation), quality-evaluation systems, intelligent case-routing services, and privacy-preserving data tooling. The focus is on building and deploying AI solutions within an enterprise customer support context.

What you'd actually do

  1. An agent platform - an extensible, Model Context Protocol (MCP)–based system of AI diagnostic skills integrated with enterprise data services, run inside engineers' existing tools (CLI, VS Code, and chat).
  2. Production engineering rails - CI-enforced quality gates, evaluation regression testing, comprehensive data science telemetry, and supply-chain security that let us ship AI safely and at scale. Every system is instrumented to surface performance, adoption, and quality signals in real time.
  3. Language-model pipelines - training, fine-tuning (including RLHF), and automated evaluation of models that reason over and assess support cases.
  4. Quality-evaluation systems - rubric-based, LLM-as-judge evaluation that scores case handling and surfaces coaching and compliance signals.
  5. Intelligent case-routing services - classification and ranking models that route cases to the right team and catch misrouted cases early.

Skills

Required

  • Bachelor's Degree in Computer Science, Information Technology (IT), or related field AND 3+ years technical support, technical consulting experience, or information technology experience OR equivalent experience.

Nice to have

  • 3+ years of experience developing and shipping production software services using TypeScript/Node.js, Python, or C#/.NET.
  • Experience building AI-powered applications, including large language models (LLMs), AI agents, prompt engineering, Model Context Protocol (MCP), machine learning models, or text classification/ranking systems.
  • Experience designing cloud and data solutions using Azure, large-scale datasets, Azure Data Explorer (Kusto), and retrieval technologies such as keyword, vector, or semantic search.
  • Experience evaluating and improving AI systems through model evaluation frameworks, RLHF, preference-based training, LLM-as-a-judge methodologies, labeled datasets, or benchmark development.
  • Experience implementing software quality, security, and DevOps practices, including automated testing, code reviews, CI/CD pipelines, GitHub Actions, CodeQL, dependency management, secret scanning, and branch governance.
  • Experience developing customer-facing or enterprise applications, including solutions for privacy, compliance, PII detection, data redaction, customer support, or operational workflows.

What the JD emphasized

  • shipping AI safely and at scale
  • production engineering
  • scalable AI and software systems
  • designing cloud and data solutions
  • developing and shipping production software services
  • building AI-powered applications
  • evaluating and improving AI systems

Other signals

  • shipping AI systems
  • agent platform
  • production engineering for AI
  • language model pipelines
  • quality-evaluation systems
  • intelligent case-routing
  • privacy-preserving data tooling