You'll take Generative AI concepts from proof of concept to production, designing, developing, deploying, and continuously improving AI-powered applications that solve real-world operational problems. If you enjoy building with the latest LLMs, experimenting with emerging AI technologies, and deploying secure cloud-native solutions in Azure, we'd like to meet you.
What You'll Do- Design and build Generative AI applications using commercial and open-source Large Language Models (LLMs).
- Develop proof-of-concepts that evolve into production-ready software.
- Architect Retrieval-Augmented Generation (RAG) solutions using vector databases and enterprise data sources.
- Build intelligent agents and AI workflows capable of reasoning, automation, and decision support.
- Develop REST APIs and backend services supporting AI applications.
- Deploy scalable AI solutions within Microsoft Azure environments.
- Design cloud-native architectures using Azure AI Services, Azure OpenAI, Azure Kubernetes Service (AKS), Azure Functions, and Azure Storage.
- Build CI/CD pipelines supporting rapid AI deployment and model iteration.
- Integrate AI capabilities into existing enterprise applications and mission systems.
- Evaluate emerging AI technologies and rapidly prototype new capabilities.
- Collaborate with software engineers, cloud architects, data scientists, and mission stakeholders to deliver innovative solutions.
- Optimize model performance, latency, scalability, security, and cost.
- Implement responsible AI practices, prompt engineering strategies, guardrails, and model evaluation techniques.
Requirements- Active Top Secret Clearance
- 5+ years of software development experience.
- 2+ years developing Generative AI or Machine Learning applications.
- Strong Python development experience.
- Experience working with Large Language Models including GPT, Llama, Claude, Mistral, or similar models.
- Experience with prompt engineering and AI workflow development.
- Experience building APIs using FastAPI, Flask, or similar frameworks.
- Experience deploying cloud-native applications in Microsoft Azure.
- Familiarity with containerization technologies including Docker and Kubernetes.
- Experience with Git, CI/CD pipelines, and DevSecOps practices.
- Strong understanding of software architecture and distributed systems.
Preferred Qualifications- Experience with Azure OpenAI Service.
- Experience building Retrieval-Augmented Generation (RAG) systems.
- Knowledge of LangChain, LangGraph, Semantic Kernel, LlamaIndex, or similar orchestration frameworks.
- Experience with vector databases such as Pinecone, Milvus, pgvector, Azure AI Search, or Chroma.
- Experience developing AI agents and autonomous workflows.
- Familiarity with MCP (Model Context Protocol) and agent interoperability concepts.
- Experience with model evaluation, observability, and prompt optimization.
- Experience deploying AI solutions in secure or classified environments.
- Familiarity with Infrastructure as Code using Terraform or Bicep.
- Knowledge of Azure Machine Learning, Azure AI Foundry, or Azure Cognitive Services.
