ML Ops Engineer - Clearance Required

Overview

LMI is seeking a Machine Learning Operations Engineer (ML Ops Engineer) to support the development of cutting‑edge AI/ML solutions in collaboration with the Army’s AI2C organization. This role emphasizes integrating machine learning workflows into scalable, efficient applications while addressing operational needs for the United States Army. The ML Ops Engineer will work at the intersection of advanced AI/ML development, machine learning system deployment, and mission‑critical applications, ensuring end‑to‑end lifecycle management of AI capabilities.

This position provides an exciting opportunity to collaborate directly with the Army to design cutting‑edge generative AI tools and machine learning systems to empower their operations and decision‑making. Candidates should thrive in a fast‑paced, collaborative environment and demonstrate technical creativity, continuous learning, and problem‑solving expertise.

LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial‑grade platforms and mission‑ready AI to federal agencies at commercial speed.

Leveraging our mission‑ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors—helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.

Responsibilities:

• Build, train, validate, and evaluate machine learning models using technologies such as Scikit‑Learn, Tensor Flow, or similar tools.
• Research, develop, and implement generative AI applications, ensuring that models address complex real‑world challenges effectively.
• Deploy machine learning models to web‑based applications and integrate them into operational environments.
  • Operationalize generative AI systems by developing robust, scalable pipelines for deployment across multiple environments.
  • Design and implement advanced data manipulation and pipelining workflows using tools such as Pandas and PySpark to support model training and analysis.
  • Support CI/CD pipelines tailored for ML model development and deployment.
  • Work alongside other engineering and Dev Sec Ops  teams to support scalable cloud‑based deployments.
  • Collaborate directly with Army stakeholders to identify strategic opportunities for ML integration, addressing challenges and providing innovative technical solutions.
  • Assist product leads in translating operational needs and feedback into actionable technical requirements and strategies.
  • Mentor junior team members, guiding their ML and MLOps skill development while contributing to process improvements.
  • Lead discussions on architecture, system design, technology adoption, and team development to strengthen LMI’s ML capabilities.
  • Build and maintain strong relationships with government customers and stakeholders through hybrid on‑site engagement.
  • Contribute to technical narratives for proposals, white papers, and strategic documentation for expanding AI/ML and ML Ops projects within Army domains.

Percentage of

Travel Required:

10%

Minimum Qualifications:

• Bachelor’s degree in Computer Science, Data Science, Software Engineering, or a related field.
• 3+ years of experience in machine learning engineering, with particular emphasis on MLOps, model development, and deployment.
• Demonstrated expertise in data manipulation & pipelining technologies, such as Pandas or PySpark.
• Hands‑on experience developing machine learning models using tools such as Scikit‑Learn, MLlib, Tensor Flow, PyTorch, etc.
• Practical experience in deploying AI/ML models in production web‑based applications.
• Advanced proficiency with Python and Python‑based web frameworks (e.g., Flask, Django, FastAPI, etc.).
• Strong understanding and hands‑on experience with containerization technologies, such as Docker and Kubernetes.
• Familiarity with Agile…