Full Stack Python Developer, Generative AI

We are hiring a hands on full stack developer to take Generative AI proofs of concept out of the sandbox and into a live federal production environment. This is a build role, not an architecture role: you will be writing the Python, wiring the models, shipping the front end, and standing behind what you deploy. The work supports a federal mission serving the American public, so we are looking for someone who is genuinely curious about what AI can do for people and equally serious about the risks of getting it wrong.What You Will DoSit with stakeholders to shape rough Generative AI use cases into something buildable, then build it end to endDesign and ship proofs of concept on Azure AI and AWS Bedrock, front end through back endStand up secure, scalable web applications that integrate Generative AI models and APIsPrototype fast, take the feedback, and iterate again the same weekIntegrate cloud services and own the deployment pipeline for what you buildDocument your designs and architecture so the next engineer is not reverse engineering your workPartner with data scientists, UI/UX designers, and project managers as one delivery teamRun code reviews, write the tests, and chase down the defects before the demoRequiredMaster's plus 5 years, Bachelor's plus 7 years, or 13 or more years of experience in lieu of a degree5 or more years in full stack development across both front end and back end3 or more years writing production Python, with current language practicesHands on application development on Microsoft Azure and/or AWSDemonstrated integration of AI/ML models or APIs into working applicationsPython plus JavaScript (React, Angular, or Node.js) or comparable stackRESTful APIs, microservices architecture, and containerization with Docker or OpenShiftUnit testing, continuous integration, and version control as daily habits, not aspirationsAbility to explain a Generative AI concept clearly to someone who does not write codePreferredAzure OpenAI Service, Azure AI Search, and Azure Vision in production, not just in a tutorialWorking command of Retrieval Augmented Generation: chunking, embeddings, retrieval qualityevaluationPython web frameworks for APIs and backend services (FastAPI, Flask, Django)Data preparation for RAG ingestion: cleaning, normalizing, and structuring source contentGenerative AI deployed to production on Azure infrastructure, including monitoring and securityJenkins, Azure DevOps, Bitbucket, and CI/CD practices that get quality releases out the doorBroader Azure AI depth: Azure Machine Learning, Cognitive Services, Databricks, Synapse AnalyticsCompliance and security practice around sensitive personal dataC# or JavaData science tooling: Jupyter, pandas, PyTorchA real point of view on ethical AI: fairness, transparency, accountability, and privacy