[Hiring] AI Developer IV @Select Source Solutions

Role Description

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.

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

Requirements

• 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

Benefits

• 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.