Agentic AI Machine Learning Engineer - Secret Clearance

Description

Job Title: Agentic AI Machine Learning Engineer - Secret Clearance

Position Type: Full-Time W2, Direct Hire

Pay: $99,000.00 to $225,000.00

Location : On-Site, Must be in one of the following locations: Washington D.C., Arlington, VA, McLean, VA, St. Louis, MO, Denver, CO, or Colorado Springs, CO.

Years of Experience Required:

Overview

As an experienced machine learning engineer, you understand good software is more than just a good user experience. To compete in today's technical landscape, mission-oriented machine learning solutions must be architected, designed, and built to handle fast-moving data, to seamlessly scale with infrastructure based on system usage, and to expand based on evolving mission requirements. We're looking for an engineer like you to create artificial intelligence (AI) and machine learning (ML) enabled solutions that help solve our toughest challenges facing the Defense and Intelligence sectors.

On our team, you'll design, create, and implement complete AI systems that will transform client operations, increase data accessibility, and optimize AI and ML systems. You'll ensure that your team's solutions consider the broader ecosystem and operating environment as well as future functionality and enhancements. Additionally, you'll deepen your skill set in areas like software engineering, machine learning operations (MLOps), and software deployment and integration into a variety of different mission environments.

Key Responsibilities

• Design, develop, and implement AI/ML systems that enhance mission effectiveness for Defense and Intelligence clients.
• Build and operationalize agentic AI solutions , integrating frameworks such as LangChain, LangGraph, PydanticAI, or LlamaIndex.
• Architect scalable and resilient ML applications capable of handling fast-moving data and evolving mission requirements.
• Train, deploy, and maintain production-grade models across multiple data modalities, leveraging cloud platforms like AWS and Azure.
• Deploy ML solutions in containerized environments using tools such as Docker and Kubernetes.
• Integrate AI agents with APIs, cloud services, and databases to support real-world mission use cases.
• Apply MLOps, GitOps, and CI/CD practices to streamline deployment, monitoring, and continuous improvement of ML systems.
• Evaluate architectural tradeoffs and design service-based applications optimized for performance and scalability.
• Collaborate with cross-functional teams including data scientists, engineers, and mission stakeholders to deliver end-to-end solutions.
• Experiment with and apply emerging AI/ML approaches -including LLMs, deep learning, and reinforcement learning-to address complex operational challenges.
• Communicate technical concepts clearly to both technical and non-technical stakeholders to align solutions with mission needs.

Required Skills & Qualifications

• 3+ years of experience as an ML engineer and building production-grade ML solutions, including work involving LLMs, agents, or complex automation frameworks
• 3+ years of experience working within data science or data research in a professional or academic environment, and training or deploying models across multiple modalities of data
• 3+ years of experience working in cloud environments, including AWS and Azure
• 2+ years of experience deploying and integrating production-grade ML models using tools such as Docker and Kubernetes
• Experience with Large Language Models (LLMs), Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL)
• Experience with tools and AI agent frameworks such as LangChain, LangGraph, PydanticAI, or llamaindex
• Experience in connecting Agents to APIs, Cloud platforms, or databases, and MLOps, GitOps, and CI/CD tooling
• Experience evaluating architectural tradeoffs and designing robust service-based software applications for scalable use
• Secret clearance
• Bachelor's degree

Preferred Qualifications

• Experience with programming, including ML frameworks such as TensorFlow, PyTorch, llama.cpp, and vLLM
• Experience with client engagements, client-facing project work, and business development
• Experience with project work in deep learning, computer vision, NLP, or signal processing
• Experience deploying and managing data brokering solutions, including Kafka, Red Panda, Confluent, and other related services
• Ability to adapt in a rapidly changing environment
• Possession of excellent verbal and written communication skills
• Possession of excellent interpersonal, analytical, problem-solving, and organizational skills
• Master's degree