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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied AI Engineer - Global Health & AI (REMOTE ROLE) - **Company:** ICF Incorporated, L.L.C. - **Location:** Rockville, MD, United States - **Experience:** Experienced - **Salary:** $98,614.0 - $167,644.0 - **Contract:** Permanent contract - **Skills:** AI Evaluation, JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Batch Processing, Data as a Services, Cursor, Python (Programming Language), Regression Testing, Software Engineering, TypeScript, Google Cloud, Cloud Platform System, GitHub Copilot, Retrieval-Augmented Generation, Large Language Models, Claude Code, Backend, Agentic-AI, Build Management, Information Technology, Low Latency, Production Code, Free and Open-Source Software, OpenAI Codex, Programming Languages - **Published:** October 9, 2026 - **Apply:** https://www.techcareers.com/job.asp?id=3426226172&tx=DT11202UHT&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent years experience) * Minimum 4 years of professional software development experience, including building LLM-powered systems that real users relied on in production * U.S. citizenship is required by federal government contract Additional Required Skills and Experience * Proficiency in Python or another modern programming language (e.g., JavaScript/TypeScript, Go) for back-end and AI application development * Hands-on experience building with frontier-model APIs (from any provider, or through any cloud platform), including tool use and retrieval-augmented generation * Demonstrated experience designing LLM evaluations, including test-set construction, automated and human grading, regression testing, and ship/no-ship decisions. * Working knowledge of LLM cost, latency, and model-selection trade-offs * Experience building and deploying on a major cloud platform (e.g., AWS, Azure, or Google Cloud) * Demonstrated experience using agentic coding tools, such as Claude Code, OpenAI Codex, Cursor, Antigravity, or GitHub Copilot agent mode, to specify, review, test, and take ownership of production code. * Experience with agent frameworks and AI evaluation tooling. Preferred Skills * Experience with tasks where ground truth came from subject matter experts (for example, statisticians, clinicians, scientists, or methodologists) * Experience with grounded generation over structured or tabular data, or with LLM-assisted statistical analysis * Experience with multilingual or low-resource-language NLP, translation, or speech * Experience deploying open-weight models * Experience with sensitive or regulated data, such as health data or human-subjects research * Familiarity with model governance, monitoring, and responsible AI practice * Experience in global health, international development, or low- and middle-income country settings * Public work such as open-source contributions, published evaluations, or technical writing about systems you built undefined Professional Skills * Explains AI trade-offs clearly to technical and non-technical audiences, including funders, survey experts, and partner-government staff * Collaborates effectively across disciplines, cultures, time zones, and organizational levels while treating domain experts as the source of truth * Communicates uncertainty, limitations, and negative results candidly.Self-motivated * Finds ways to add value without a lot of direct supervision #Indeed #LI-CCI #GHDAI Bot and Third-Party Applications ## Description * Design and build LLM pipelines and agents for survey workflows, including: * an AI-assisted data-to-report pipeline * natural-language analysis of survey data * multilingual questionnaire translation * field support tools for interviewers and supervisors * Build evaluation harnesses as first-class deliverables. Work with the Technical Lead and survey experts to define acceptance criteria before the work is judged. Build test sets from expert-produced ground truth and run the non-inferiority comparisons that decide whether a tool moves forward. * Route work across models based on reasoning complexity, volume, cost, latency, and data-residency requirements. * Manage inference cost by tracking token usage, context limits, prompt caching, and batch processing, and account for these operating costs in model-selection decisions. * Connect models to data services and to the applications built by full stack developers. * Use agentic coding tools to produce software from clear specifications, then review, test, and own the resulting code. * Monitor production systems, conduct disciplined error analysis, and report positive and negative results. * Document methods and results for public release , including negative results. Help transfer tools to partner-country institutions so they can run them on their own.