AI Systems Engineer

Tria Federal

14h

About The Position

We are looking for a highly skilled AI Systems Engineer who will be part of a collaborative and agile team that supports and builds modern, usable, and responsive applications for mission-critical U.S. federal government health IT solutions. The Senior AI Systems Engineer will operate in a fast paced innovation environment, rapidly developing and validating proof of concepts for advanced AI and machine learning use cases. The role centers on experimenting with cutting edge analytics tools, integrating them into a cloud based analytics ecosystem, and helping move successful concepts toward operational deployment. Responsibilities include hands on data exploration, prototyping, workflow automation, and contributing to secure, well governed MLOps practices. The Software Engineer will work closely with engineering and product teams to evaluate emerging tools, build technical demonstrations, document integrations, and communicate platform updates in a highly collaborative setting. data platform fluency, and agility to support cross-functional teams.

Requirements

5+ years of relevant software / systems engineering experience
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.
Experience with version control and modern CI/CD practices (e.g., Git/GitHub), including automated testing, deployment pipelines, and release management for production systems.
Ability to obtain/maintain a Public Trust clearance.

Nice To Haves

Experience building or contributing to multi agent systems or coordinated agent workflows.
Familiarity with frameworks such as LangChain, LlamaIndex, Strands Agents, LangGraph, CrewAI.
Knowledge of evaluating agent performance, implementing guardrails, or designing safe action execution patterns.
Hands on work with vector databases, embedding models, or advanced retrieval techniques.
Experience integrating agentic components into large scale analytics or data platforms (e.g., Databricks, Snowflake).
Background in data exploration or data modeling to support agent driven decision processes.
Experience building AI focused prototypes or experimenting with emerging agentic patterns in rapid iteration environments.
Familiarity with optimization techniques such as model routing, response validation layers, or inference acceleration.
Familiarity with 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.