ML Ops Engineer - Clearance Required

Responsibilities

• Build, train, validate, and evaluate machine learning models using technologies such as Scikit-Learn, TensorFlow, 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 DevSecOps 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.

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, TensorFlow, 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 or Scrum methodologies, CI/CD practices, and version control systems (e.g., Git).
• Comfort operating in ambiguous and dynamic environments requiring proactive problem-solving.
• Active Secret Clearance required.

Additional Preferred Qualifications

• Master’s degree in Computer Science, Software Engineering, Information Systems, or related field.
• 7+ years of directly related experience.
• Proven track record using MLOps workflows (e.g., MLFlow, Kubeflow), including monitoring, orchestrating, and scaling production models.
• Hands-on deployment experience across multiple environments and platforms.
• Experience integrating machine learning and analytical tools.
• Background working in strategic planning or consultant environments supporting government or DoD clients.
• Proven track record of expanding technical scope or footprint with government customers.
• Knowledge of the Army software development process and its technologies.

Target salary range

$110,075 - $185,138