AI Developer IV, Scientific and Environmental Programs (Remote)

AI Developer IV, Scientific and Environmental Programs (Remote) About the Opportunity The AI Developer IV will join the Product and Services team as a senior, hands-on developer building production AI solutions for federal scientific and environmental programs in a remote work arrangement. This is not a general AI role. You will work directly with scientific and Earth observation data, technical mission documentation, and the researchers and program teams who depend on it. The work spans generative and agentic AI, LLM integrations, RAG and knowledge solutions, APIs, and cloud-based AI services, but the measure of success is whether those systems produce results scientists can trust. The ideal candidate combines strong production engineering skills with real experience working alongside scientific data. You understand how scientific datasets are structured, where they break, and why accuracy and provenance matter more than a polished demo. Success in this role means delivering AI solutions that hold up to scientific scrutiny, earning the trust of domain experts by speaking their language, and strengthening the team's engineering standards through high-quality code, thorough reviews, and sound development practices. What You Will Do in This Role Design, build, and deploy production AI solutions that support federal scientific and environmental programsApply LLM and generative AI solutions to scientific, geospatial, and Earth observation dataBuild RAG and knowledge retrieval solutions across technical, scientific, and mission documentation, including data ingestion, chunking, embeddings, vector search, and retrieval pipelinesWork with scientists and subject matter experts to understand research questions and translate them into working AI systemsValidate AI outputs against scientific standards, with attention to accuracy, provenance, uncertainty, and reproducibilityDevelop agentic workflows that use tools, orchestration, and multi-step reasoning to support analysis and research tasksIntegrate LLMs into applications and workflows through APIs and SDKsBuild and maintain APIs and services that expose AI capabilities to internal teams and downstream systemsBuild AI-driven analytics and automation that improve operational visibility, reporting, and decision-making for federal programsImplement responsible AI practices, including human oversight, output validation, and auditabilityDeploy and operate AI services in the cloud using infrastructure as code and CI/CD practicesEvaluate and improve AI solution quality, including accuracy, latency, cost, and reliabilityImplement monitoring, logging, and guardrails to keep AI systems observable, safe, and trustworthyWrite clean, tested, well-documented code and participate in code reviewsPerform other duties and responsibilities as assignedWhat You Will Bring Basic Qualifications Bachelor's degree in Computer Science, Engineering, a physical, environmental, or Earth science, or a related field, or equivalent practical experience8+ years of professional software development experience, including deploying solutions to productionDemonstrated experience working with scientific, geospatial, environmental, or Earth observation data in a professional or research settingExperience collaborating directly with scientists, researchers, or technical domain expertsStrong proficiency in Python, including the scientific Python ecosystem (e.g., NumPy, pandas, xarray)Hands-on experience building solutions with LLMs and generative AI (e.g., prompt design, function/tool calling, structured outputs)Experience building RAG or knowledge-based solutions, including embeddings and vector databasesExperience building AI solutions in federal or other regulated environments, with attention to data security, privacy, and compliance (e.g., NIST, FedRAMP)Experience building APIs and services (e.g., FastAPI, Flask)Hands-on experience deploying applications and AI services in a major cloud environment (AWS, Azure, or GCP)Experience with CI/CD tooling and infrastructure as codeSolid software development fundamentals: testing, version control (Git), code review, and documentationStrong communication skills and the ability to collaborate in a cross-functional environmentPreferred Qualifications Advanced degree or research background in a scientific disciplineExperience with common scientific and geospatial data formats (e.g., NetCDF, HDF5, GeoTIFF) and geospatial toolsExperience supporting federal science, research, or environmental missionsFamiliarity with classical ML techniques and frameworks (e.g., scikit-learn, PyTorch), particularly as applied to scientific dataExperience building agentic AI systems with frameworks such as LangChain, LlamaIndex, or similarExperience with cloud AI platforms and managed model services (e.g., AWS Bedrock, Azure OpenAI, Vertex AI)Experience with AWS AI services such as Amazon Bedrock and SageMakerExperience evaluating and monitoring LLM applicationsExperience with