Remote ML Ops Engineer — Generative AI for Defense

LMI Government Consulting; Pittsburgh, Northern (Allegheny County, KY); Hybrid; USD 110,000 - 185,000; Full time

Job summary

LMI Government Consulting is seeking a Machine Learning Operations Engineer (ML Ops Engineer) to support Army AI/ML solutions. This role focuses on integrating ML workflows into scalable, secure applications and managing end-to-end lifecycle of AI capabilities.

The position involves collaboration with Army stakeholders, building robust ML pipelines, deploying models in production, and ensuring agile, scalable DevSecOps practices across cloud environments.

Qualifications

• Bachelor’s degree in CS, DS, or SE.
• 3+ years of ML engineering with emphasis on MLOps, model development and deployment.
• Hands-on with Pandas, PySpark for data manipulation and pipelines.
• Experience deploying AI/ML models in production web apps.
• Strong Python skills and Python web frameworks (Flask, Django, FastAPI).
• Experience with Docker, Kubernetes, CI/CD, and Git.

Responsibilities

• Build, train, validate, and evaluate ML models using Scikit-Learn, TensorFlow, or similar.
• Develop and deploy generative AI applications addressing real-world challenges.
• Deploy ML models to web-based applications and integrate into operational environments.
• Operationalize AI systems with scalable pipelines across environments.
• Collaborate with Army stakeholders to identify ML integration opportunities.

Skills

MLOps

Python

Docker

Kubernetes

CI/CD

Cloud deployments

Education

Bachelor’s degree in Computer Science, Data Science, or Software Engineering

Master’s degree (preferred)

Tools

Scikit-Learn

TensorFlow

PyTorch

MLflow

Kubeflow

Job description

LMI Government Consulting is seeking a Machine Learning Operations Engineer (ML Ops Engineer) to support Army AI/ML solutions. This role focuses on integrating ML workflows into scalable, secure applications and managing end-to-end lifecycle of AI capabilities.

The position involves collaboration with Army stakeholders, building robust ML pipelines, deploying models in production, and ensuring agile, scalable DevSecOps practices across cloud environments.