Minimum of 8 years with BS/BA; Minimum of 6 years with MS/MA; Minimum of 3 years with PhD
Mandatory Requirements:
• Strong software engineering background with deep experience building production‑grade applications and services.
• Expertise developing agentic AI systems, including planning, tool‑use, multi‑step reasoning, workflow execution, or autonomous decisioning logic.
• Hands‑on experience designing and implementing MCP‑based integrations, tool interfaces, or model‑driven service frameworks.
• Ability to translate ambiguous business or mission requirements into scalable AI-driven solutions, balancing technical feasibility with real-world impact.
• Proficiency with LLM development practices including fine‑tuning, RAG integration, prompt engineering, and interaction models for agent workflows.
• Strong Python development skills and familiarity with distributed compute environments, APIs, microservices, and cloud‑native architectures.
• Experience integrating agents or LLM‑driven components into cloud platforms (Azure, AWS, GCP) or large‑scale data ecosystems.
• Understanding of LLMOps/MLOps principles including versioning, testing, deployment automation, monitoring, and governance for agentic systems.
• Demonstrated ability to lead solution design, mentor developers, and communicate complex AI architectures to technical and non‑technical stakeholders.
• Version control and modern CI/CD practices (e.g., Git/GitHub), including automated testing, deployment pipelines, & release management for production systems.
• US Citizen with the ability to obtain/maintain a Public Trust clearance.
Preferred Requirements:
• Experience building multi‑agent systems, agent swarms, or coordinated reasoning frameworks.
• Familiarity with advanced tool‑calling strategies, including dynamic tool selection, function‑call planning, or graph‑structured task planners.
• Experience with structured LLM evaluation methods, agent benchmarking, or test harnesses for autonomous systems.
• Knowledge of performance optimization techniques for LLMs and agents, including caching, model distillation, model routing, or accelerated inference.
• Background integrating agentic components with large‑scale data or analytics platforms (e.g., Databricks, Snowflake, Spark).
• Hands‑on experience developing innovative POCs or experimental agentic architectures in fast‑paced R&D environments.
• Familiarity with emerging agentic frameworks such as Strands Agents, LangGraph, CrewAI, etc.
• Exposure to safety‑oriented design patterns for autonomous systems, including guardrails, validation layers, or constrained‑action frameworks.
• Experience designing and building secure, compliance-aware systems that handle sensitive data in accordance with HIPAA and federal security standards, including implementation of encryption, access controls, auditability, and governance for protected health information (PHI) within AI/LLM workflows.
