Applied AI Engineer

Key Responsibilities

• 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.

Minimum Required Experience

• 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

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

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