- Define and drive the technical direction of mission-critical AI systems supporting public health and safety
- Lead the design and implementation of scalable ML and generative AI solutions, including AI agents, RAG systems, and evaluation frameworks
- Serve as a technical authority across a community of engineers
- Partner with data engineers, architects, product owners, and stakeholders to shape enterprise AI strategy, architecture standards, and delivery practices
- Design AI systems end-to-end, including data pipelines, modeling, agent orchestration, memory or state management, retrieval optimization, and production deployment
- Lead AI evaluation strategies, prompt engineering workflows, and governance frameworks
- Ensure systems are performant, explainable, secure, and compliant with public health data privacy and ethical standards
- Lead development of scalable AI/ML systems using PySpark and Palantir Foundry for public health mission applications
- Architect generative AI solutions, AI agents, MCP-enabled workflows, Prompt Buddy integrations, and RAG pipelines
- Define evaluation frameworks for performance benchmarking, retrieval optimization, safety validation, and production monitoring
- Design and implement agent architectures with memory, state management, orchestration, and structured or unstructured data interaction
- Integrate Codex, Claude, and OpenEvidence into enterprise AI workflows and platforms
- Establish data governance, privacy, anonymization, documentation, and ethical AI standards across AI/ML systems
Requirements
- 5+ years of experience designing, developing, and deploying AI/ML solutions using Python
- 5+ years of experience with generative AI, LLMs, AI agents, or RAG systems in enterprise environments
- 3+ years of experience with AI agents, MCP frameworks, or AI evaluation strategy development
- Experience with ML frameworks such as TensorFlow or PyTorch for production model development
- Experience with data engineering using PySpark, SQL, and Palantir Foundry, including Foundry AIP
- Experience managing a technical team
- Experience with MLOps platforms such as MLflow
- Experience with cloud environments such as Azure or Databricks
- Experience integrating or working with Codex, Claude, Prompt Buddy, or OpenEvidence in enterprise settings
- Knowledge of public health, healthcare, or government data systems and associated governance practices
- Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements
- Bachelor's degree
Core Competencies
Demonstrates expertise in designing and implementing scalable AI/ML solutions, with a strong focus on generative AI, AI agents, and data governance in public health contexts. Proven ability to lead technical teams and establish ethical AI standards while ensuring compliance with data privacy regulations.
Highest-signal resume keywords
- AI/ML Solution Development
- Generative AI Expertise
- Data Engineering with PySpark
- MLOps Experience
- Public Health Data Knowledge
ATS Optimization Keywords
Hard Skills
- Python Programming
- Machine Learning Frameworks
- AI Evaluation Strategy Development
- Data Pipeline Design
- Agent Architecture Design
Soft Skills
- Team Leadership
- Stakeholder Collaboration
- Technical Authority
Certifications & Qualifications
- Bachelor's Degree
- Public Trust or Suitability/Fitness Determination
Industry Keywords
- Public Health
- Healthcare Data Systems
- Government Data Systems
- Data Privacy
- Ethical AI Standards
Tools & Technologies
- PySpark
- Palantir Foundry
- TensorFlow
- PyTorch
- MLflow