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 programs
Apply LLM and generative AI solutions to scientific, geospatial, and Earth observation data
Build RAG and knowledge retrieval solutions across technical, scientific, and mission documentation, including data ingestion, chunking, embeddings, vector search, and retrieval pipelines
Work with scientists and subject matter experts to understand research questions and translate them into working AI systems
Validate AI outputs against scientific standards, with attention to accuracy, provenance, uncertainty, and reproducibility
Develop agentic workflows that use tools, orchestration, and multi-step reasoning to support analysis and research tasks
Integrate LLMs into applications and workflows through APIs and SDKs
Build and maintain APIs and services that expose AI capabilities to internal teams and downstream systems
Build AI-driven analytics and automation that improve operational visibility, reporting, and decision-making for federal programs
Implement responsible AI practices, including human oversight, output validation, and auditability
Deploy and operate AI services in the cloud using infrastructure as code and CI/CD practices
Evaluate and improve AI solution quality, including accuracy, latency, cost, and reliability
Implement monitoring, logging, and guardrails to keep AI systems observable, safe, and trustworthy
Write clean, tested, well-documented code and participate in code reviews
Perform other duties and responsibilities as assigned
What 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 experience
8+ years of professional software development experience, including deploying solutions to production
Demonstrated experience working with scientific, geospatial, environmental, or Earth observation data in a professional or research setting
Experience collaborating directly with scientists, researchers, or technical domain experts
Strong 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 databases
Experience 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 code
Solid software development fundamentals: testing, version control (Git), code review, and documentation
Strong communication skills and the ability to collaborate in a cross-functional environment
Preferred Qualifications
Advanced degree or research background in a scientific discipline
Experience with common scientific and geospatial data formats (e.g., NetCDF, HDF5, GeoTIFF) and geospatial tools
Experience supporting federal science, research, or environmental missions
Familiarity with classical ML techniques and frameworks (e.g., scikit-learn, PyTorch), particularly as applied to scientific data
Experience building agentic AI systems with frameworks such as LangChain, LlamaIndex, or similar
Experience 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 SageMaker
Experience evaluating and monitoring LLM applications
Experience with containerization (Docker) and container orchestration
Cloud or AI/ML certification
Work Authorization/Security Clearance Requirements
Ability to obtain a security clearance.
Work Environment
This work is normally completed in a remote environment.
Physical Demands
Prolonged periods of sitting at a desk and working on a computer. Must be able to access and navigate each department at the organization's and client facilities.
Travel Required
No
Proficiency Requirement
The employee is expected to demonstrate proficiency in all essential job functions, tools, and processes related to this position within the first 90 days of employment. This includes acquiring a thorough understanding of job-specific responsibilities, systems, and workflows as outlined during onboarding and training. Failure to meet this requirement may result in additional training, reassessment, or other actions as deemed necessary by management.
