AI Workflow Engineer

Computercraft Corporation•Bethesda, MD

2d•Remote

About The Position

We are looking for an AI Workflow Engineer to design, build, and operationalize AI-enabled workflows that integrate LLMs, retrieval systems, and biomedical data to support scientific and operational use cases for the National Institutes of Health (NIH) scientific databases. This is a part-time or full-time opportunity, available as either a contract role or a staff position, that will be 6 months in duration with the potential for extension. Candidates are not required to be based in the Washington, DC, area but must be willing to travel as needed. Candidates must be authorized to work in the United States. The AI Workflow Engineer will work closely with stakeholders to prototype and implement high-impact pilot use cases, emphasizing practical usability, scalability, and alignment with real-world scientific workflows. This role is central to enabling structured, traceable, and explainable AI-assisted workflows that enhance productivity, improve data integration, and support reproducible scientific outcomes.

Requirements

Bachelor’s or master’s degree in computer science, artificial intelligence, bioinformatics, data science, or related field
3–5+ years of experience building AI/ML-enabled applications or workflows in real-world environments
Experience with LLMs and generative AI
Experience with RAG systems
Experience with Agent-based architectures and multi-step AI workflows
Experience with API development and system integration (e.g., Python, REST)
Experience working with structured and unstructured data systems
Ability to design and evaluate end-to-end AI workflows, not just individual components

Nice To Haves

Experience with tool-use frameworks or MCP-like architectures
Experience integrating AI with biomedical or bioinformatics data systems (e.g., NCBI resources, genomic data, ontologies)
Familiarity with entity normalization, knowledge graphs, or semantic data integration
Experience with vector databases and hybrid retrieval systems
Experience adapting models to domain-specific contexts (e.g., prompt tuning, retrieval tuning, fine-tuning)
Experience working in federal, research, or regulated environments

Responsibilities

Design and implement end-to-end AI-enabled workflows supporting biomedical research, data analysis, and operational processes
Develop agent-based systems that orchestrate multi-step reasoning, tool use, and interaction with structured and unstructured data sources
Build and optimize RAG pipelines, including vector search, hybrid retrieval, and integration with structured data sources
Implement tool-use frameworks (e.g., MCP or equivalent architectures) to enable LLM interaction with APIs, databases, and internal systems
Develop workflows that integrate with NCBI data systems, APIs, and knowledge graphs, enabling structured retrieval and synthesis across resources
Design approaches for entity grounding, normalization, and linking across biomedical concepts (e.g., genes, variants, diseases, publications)
Apply AI methods to support data harmonization and linkage across NCBI datasets, including structured and semi-structured data
Prototype and evaluate domain-adapted LLM approaches, including prompt engineering, retrieval optimization, and model re-weighting or fine-tuning where appropriate
Ensure AI workflows reflect biomedical and bioinformatics contexts, including use of ontologies, identifiers, and domain-specific data structures
Develop traceable and reproducible AI workflows, including capture of inputs, prompts, intermediate steps, and outputs
Design and implement evaluation frameworks to assess performance, reliability, and scientific validity of AI-assisted workflows
Establish methods to document provenance, assumptions, and transformations in AI-driven outputs
Develop test harnesses and metrics for quality, robustness, latency, and regression testing
Collaborate with stakeholders to identify and prioritize high-impact AI pilot use cases
Build and iterate on prototypes, supporting real-world deployment and refinement
Design integration approaches for incorporating AI workflows into existing systems and processes
Provide technical input on scalability, maintainability, and operationalization
Work closely with training/instruction staff to ensure workflows are usable, understandable, and transferable
Provide technical guidance and mentorship to staff implementing AI solutions
Contribute to development of playbooks, templates, and reusable workflow components