ML Ops Engineer - Clearance Required

Summary

LMI is a digital solutions provider delivering AI and technology solutions for government agencies. The ML Ops Engineer will develop, deploy, and manage machine learning and generative AI systems, build scalable data and deployment pipelines, collaborate with Army stakeholders, and support cloud-based operational environments.

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

Skills

• 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
• 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

Qualifications

Must Haves

• 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

Nice to Haves

• 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