Agentic AI Machine Learning Engineer - TS SCI

Description

Job Title: Agentic AI Machine Learning Engineer - TS SCI

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, St. Louis, MO, Denver, CO, or Colorado Springs, CO.

Years of Experience Required: 3

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.

Join us. The world can't wait.

Key Responsibilities

• Design, develop, and deploy AI/ML solutions that support Defense and Intelligence sector missions.
• Architect scalable machine learning systems that can handle fast-moving data, evolving mission requirements, and dynamic infrastructure demands.
• Build and operationalize AI agents and automation frameworks, leveraging tools such as LangChain, LangGraph, LlamaIndex, or PydanticAI.
• Integrate large language models (LLMs), deep learning, and reinforcement learning into real-world applications across multiple data modalities.
• Connect AI agents to APIs, cloud platforms, and databases to enable mission-ready interoperability.
• Implement MLOps practices for training, testing, deploying, and monitoring production-grade ML solutions.
• Evaluate architectural tradeoffs and design service-based applications that are robust, secure, and scalable.
• Deploy ML models in cloud and containerized environments, using platforms like AWS, Azure, Docker, and Kubernetes.
• Enhance data accessibility and system performance by integrating AI/ML into client operations and workflows.
• Collaborate with cross-functional teams including data scientists, software engineers, solution architects, and mission stakeholders.
• Continuously research and experiment with emerging AI/ML techniques, frameworks, and architectures to drive innovation.
• Communicate complex technical concepts clearly to non-technical stakeholders, ensuring alignment with mission goals.

Required Skills & Qualifications

• 3+ years of experience as a 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 (LLM), Deep Learning (DL), and Reinforcement Learning (RL), and with tools and AI agent frameworks such as LangChain, LangGraph, PydanticAI, or llamaindex
• Experience in connecting Agents to APIs, Cloud platforms, or databases
• Experience evaluating architectural tradeoffs and designing robust service-based software applications for scalable use
• Experience with MLOps, GitOps, and CI/CD tooling
• TS/SCI 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
• TS/SCI clearance with a polygraph
• Master's degree