MLOps Engineer -- AI/ML Systems Deployment (TS/SCI Preferred)

Role overview

MLOps Engineer responsible for taking AI/ML systems from prototype to deployment and operational use in a secure, mission-focused environment. This is a production engineering role (not research) centered on reliable, repeatable, auditable model pipelines and inference systems.

Location & work arrangement

• Dayton, OH preferred (Cleveland, OH may be considered)
• On-site preferred; remote may be considered for clearance-ready candidates who can support secure/CAC-enabled environments and can travel as needed

Clearance / eligibility

• U.S. citizenship required
• Active TS/SCI strongly preferred (active Secret may be considered for upgrade)
• Start timing may vary based on clearance status

Responsibilities

• Operationalize AI/ML systems by deploying models into secure environments
• Move workflows into containerized pipelines and support batch and real-time inference architectures
• Build production-grade ML pipelines and own deployed systems end-to-end (not just experiments)
• Manage model versioning and lineage; use tools such as MLflow, Kubeflow, Airflow, Argo, or ClearML
• Engineer cloud-native ML infrastructure on Kubernetes
• Dockerize models and support CI/CD for AI/ML systems
• Ensure reliability via monitoring/observability (e.g., Prometheus, Grafana, OpenTelemetry)
• Address issues related to latency, drift, and resource usage
• Support secure/constrained environments (limited compute, restricted data, degraded connectivity)
• Create runbooks, documentation, and operational playbooks for repeatable operations

Requirements

• U.S. citizenship
• Background deploying ML systems or production software
• Strong Python skills
• Hands-on Docker/container experience
• Familiarity with Kubernetes / cloud-native environments
• Understanding of CI/CD
• Ability to work in secure/CAC-enabled environments

Preferred qualifications

• Active TS/SCI (or Secret with upgrade eligibility)
• Experience with ML lifecycle tools (MLflow, Kubeflow, etc.)
• Model serving / inference APIs
• Experience with LLMs/transformers
• Kubernetes-based ML workloads
• Observability tools experience
• DoD/defense background
• Exposure to edge/offline environments

Benefits (highlights)

• 100% covered certifications & training
• 401(k) with 100% match up to 6%
• Highly competitive PTO
• Comprehensive medical/dental/vision
• Life insurance + short/long-term disability
• Home office & equipment plan
• Industry-leading weekly pay schedule