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AI Automation Architect (.NET) at OurAssistants

Roles & Responsibilities

  • Strong C# / .NET Engineering
  • Azure Functions / Azure Services
  • LLM / Prompt Engineering
  • AI Workflow Orchestration

Requirements:

  • Design and implement AI-powered workflows from idea to production
  • Decide what should be handled by LLMs vs deterministic code
  • Build reliable, observable, and scalable automation on Azure
  • Ensure quality, safety, and continuous improvement of AI workflows

Job description


We’re looking for an AI Workflow Engineer / Automation Architect to design and build end-to-end AI-enabled business workflows. You’ll productionize solutions using C#/.NET on Azure Functions, integrating LLMs cleanly and safely into existing systems.

What You’ll Do

  • Design and implement AI-powered workflows from idea to production
  • Decide what should be handled by LLMs vs deterministic code
  • Build reliable, observable, and scalable automation on Azure
  • Ensure quality, safety, and continuous improvement of AI workflows

Must-Have Requirements

1. Strong C# / .NET Engineering

  • Production-grade C# with modern .NET
  • Experience with async programming, dependency injection, error handling, and structured logging
  • Writes maintainable code with unit/integration tests
  • Familiar with CI/CD and code review best practices

2. Azure Functions & Azure Services

  • Hands-on experience with Azure Functions (HTTP, queue, Service Bus triggers)
  • Familiar with Durable Functions (if applicable)
  • Practical experience with:
  • Azure Service Bus
  • Azure Storage
  • Key Vault
  • Application Insights / monitoring
  • Understands configuration, secrets, and scaling concerns

3. LLM & Prompt Engineering

  • Designs prompts with structured outputs (schemas, JSON)
  • Builds guardrails to reduce hallucinations and ambiguity
  • Implements validation, retries, fallbacks, and human-in-the-loop where needed
  • Understands real-world LLM failure modes

4. AI Workflow Orchestration

  • Breaks business processes into clear automation steps
  • Implements orchestration patterns such as:
  • Idempotency
  • Correlation IDs
  • Retries and dead-lettering
  • End-to-end observability

5. Quality & Evaluation

  • Defines evaluation datasets (“golden sets”)
  • Runs regression tests for prompts and workflows
  • Monitors quality drift and improves performance over time

Nice-to-Have

  • Power Platform (Power Automate / Power Apps)
  • Document AI / OCR (Azure Document Intelligence or similar)
  • RAG / embeddings and retrieval-based AI solutions
  • Security & governance experience:
  • PII handling
  • Prompt injection awareness
  • Audit trails and data controls

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