Hybrid MLOps Engineer: AI/ML Production & CI/CD

MANTECH; Ashburn (VA); Hybrid; USD 120,000 - 150,000; Full time

Job summary

MANTECH seeks a motivated ModelOps Engineer in Ashburn, VA. This hybrid-remote role blends two onsite days with three remote days to deliver ML-driven mission impact for CBP.

You will lead production integration of AI/ML models, optimize pipelines for real-time inference, and implement CI/CD/CT workflows while coordinating with data science and engineering teams. A robust MLOps background and cloud experience are essential.

Qualifications

• 7+ years in a technical role with ML/AI systems.
• Experience with MLOps tools and orchestration frameworks; cloud ML platforms.
• Proficiency in Python, Scala or Java; strong HPC concepts.
• Experience deploying ML models with Docker/Kubernetes; multi-cloud exposure.

Responsibilities

• Lead integration and deployment of trained AI/ML models into production environments.
• Develop and optimize model training and inference pipelines for real-time use.
• Structure automated ML model health monitoring, performance tracking, and refresh.
• Implement CI/CD/CT workflows using modeling platforms and services.
• Collaborate with Data Science and Data Engineering to build scalable feature stores.
• Research and recommend new MLOps tools for CBP environment.
• Test candidate AI/ML models and applications for operational assessment.

Skills

Python

Scala

Java

CI/CD

MLOps

Real-time inference

GPU acceleration

Docker

Kubernetes

PyTorch

TensorFlow

Keras

OpenCV

Apache Spark

Elasticsearch

PostgreSQL

Education

Bachelor's degree in Computer Science or related field

Tools

MLflow

Kubeflow

Apache Airflow

Alibi

Grafana

Prometheus

AWS SageMaker

Databricks

DataRobot

Docker

Kubernetes

PyTorch

TensorFlow

OpenCV

Spark

Elasticsearch

PostgreSQL

Job description

MANTECH seeks a motivated ModelOps Engineer in Ashburn, VA. This hybrid-remote role blends two onsite days with three remote days to deliver ML-driven mission impact for CBP.

You will lead production integration of AI/ML models, optimize pipelines for real-time inference, and implement CI/CD/CT workflows while coordinating with data science and engineering teams. A robust MLOps background and cloud experience are essential.