Applied AI Engineer

TheStaffed•Broomfield, CO

About The Position

Our client is seeking an Applied AI Engineer with 7–10 years of experience to design, build, and deploy enterprise-grade AI solutions, including generative AI and LLM-based applications, within a Microsoft Azure environment.

Requirements

  • 7–10 years of software engineering or data science experience with demonstrated expertise in Python and machine learning frameworks
  • Strong proficiency in generative AI, LLM applications, embeddings, vector databases, and agentic AI solutions
  • Hands-on experience with Microsoft Azure services: Azure OpenAI, Azure AI Services, Azure API Management, Azure Service Bus, Event Hubs, PostgreSQL, Entra ID, Key Vault, Monitor, and Application Insights
  • Advanced knowledge of machine learning operations (MLOps, LLMOps), CI/CD pipelines, REST APIs, and microservices architecture
  • Expert-level SQL and database design with PostgreSQL experience
  • Fluent English proficiency for technical communication and cross-team collaboration
  • Portfolio or proven track record demonstrating end-to-end AI solution delivery in enterprise environments

Responsibilities

  • Design, build, evaluate, and deploy AI-enabled solutions for business-critical applications using Python, PyTorch, TensorFlow, and scikit-learn
  • Develop and deploy machine learning models with strong emphasis on model evaluation for accuracy, reliability, hallucination mitigation, latency, and cost optimization
  • Build generative AI and LLM-based applications, including AI agents and Retrieval-Augmented Generation (RAG) systems with prompt engineering expertise
  • Design secure agentic AI solutions that interact safely with enterprise systems, incorporating guardrails, validation, retries, approval workflows, and human oversight
  • Integrate AI solutions with enterprise APIs, databases (PostgreSQL), and Azure messaging systems (Service Bus, Event Hubs)
  • Collaborate with cloud and platform engineering teams to productionize AI solutions, implementing MLOps and LLMOps best practices
  • Translate operational problems into practical, enterprise-integrated AI solutions aligned with business objectives
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