AI Workflow Engineer

• 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

• 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
  • RAG systems
  • Agent-based architectures and multi-step AI workflows
  • 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

• 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

Computercraft offers an excellent benefits package that includes health, dental, vision, and disability and life insurance; a 401(k) plan with matching; paid leave starting at 128 hours/year for the first 3 years of employment; and 11 paid holidays. We also offer the opportunity for a positive work–life balance with a standard 40-hour work week and the chance to work alongside a team of highly accomplished professionals.

To learn about other Computercraft job opportunities, please visit the Careers section of our website: https://www.computercraft-usa.com/.

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