Poly

Parsons Corporation•Columbia, MD

4d•$125,100 - $225,200

About The Position

Parsons is looking for an amazingly talented AI Engineer to join our team! Ready for a challenge that will keep you on the edge of Cybersecurity? Are you an experienced software professional who thrives in a fast-paced environment working with a collaborative team? If so, then apply today and be part of a team that supports the United States Cyber Command (USCC), where you will get to have significant impact on the mission of the USCC. Join our team of best-in-industry cyber warriors who are dedicated to providing superior service in support of national defense objectives.

Requirements

Bachelor's degree from an accredited college or university in a related discipline and 12+ years of experience; or a Master’s degree and 10+ years of experience
Active TS/SCI w/Poly
Ability to manage competing priorities in fast-paced mission environments
Ability to identify and escalate operational risks early
Strong understanding of machine learning concepts, model development, evaluation, and deployment.
Experience with generative AI technologies, including LLMs, prompt engineering, RAG architectures, AI agents, embeddings, and vector databases.
Knowledge of AI infrastructure, cloud platforms, APIs, containerization (Docker/Kubernetes), and scalable deployment architectures.
Proficiency in Python and common AI/ML frameworks and libraries (e.g., PyTorch, TensorFlow, scikit-learn, LangChain, LlamaIndex, Hugging Face, or similar).

Nice To Haves

Experience supporting USCYBERCOM, NSA, DoD cyber operations, or related mission environments
Experience in Cyber Security, or cloud computing

Responsibilities

Build and optimize AI-powered applications using large language models (LLMs), retrieval-augmented generation (RAG), AI agents, and other emerging AI technologies.
Design and maintain AI infrastructure, including model serving, vector databases, orchestration frameworks, APIs, and cloud-based AI services.
Apply ML Ops best practices for model lifecycle management, reproducibility, governance, and continuous improvement.
Evaluate new AI tools, frameworks, and technologies, recommending adoption where they improve capability, efficiency, or scalability.
Collaborate closely with data scientists, software engineers, DevOps engineers, subject matter experts, and stakeholders to translate requirements into technical solutions.
Participate in architecture discussions, code reviews, technical planning, and knowledge sharing across the team.
Troubleshoot and optimize AI systems for performance, scalability, reliability, and security.
Adapt quickly to evolving technologies, changing priorities, and new project requirements while maintaining high-quality deliverables.