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

Job Description

MLOps Engineer — AI/ML Systems Deployment
Location: Dayton, OH preferred
Work Arrangement: On-site preferred; remote may be considered for highly aligned, clearance-ready candidates able to support secure / CAC-enabled environments and travel as needed
Clearance: Active TS/SCI strongly preferred; active Secret may be considered for upgrade
Requirement: U.S. citizenship required

About the role

Build and Deploy Real-World AI Systems
Rackner is hiring an MLOps Engineer to move AI/ML systems from prototype → deployment → operational use in a secure, mission-focused environment. This is not a research role—this is where models become reliable, repeatable, auditable systems that run in real-world conditions.

Responsibilities

• Operate AI/ML systems and ML-enabled applications in secure, real-world environments
• Move workflows from experimentation into containerized, repeatable deployment pipelines
• Support batch and real-time inference architectures
• Bridge model development, software engineering, and platform operations
• Own the ML lifecycle
• Build and operate production-grade ML pipelines
• Support model versioning, lineage, reproducibility, and lifecycle governance
• Work with tools such as MLflow, Kubeflow, Airflow, Argo, ClearML, or similar platforms
• Deploy and support Kubernetes-based ML workloads
• Containerize models, pipelines, and services using Docker or similar tools
• Support CI/CD, automation, and repeatable deployment patterns for AI/ML systems
• Engineer for reliability
• Monitor model and system performance after deployment
• Support observability using tools such as Prometheus, Grafana, OpenTelemetry, or similar
• Detect and resolve issues related to latency, reliability, drift, degradation, or resource usage
• Help deploy AI/ML systems in secure, CAC-enabled, or constrained environments
• Optimize systems for reliability and usability beyond ideal lab conditions
• Create repeatable systems
• Develop runbooks, deployment documentation, and operational playbooks
• Build systems that can be understood, maintained, and operated by others

Requirements

• Core Experience:
• Background in deploying ML systems, AI-enabled applications, or production software
• Strong programming skills in Python
• Hands-on work with Docker, containers, or containerized deployment
• Familiarity with Kubernetes or cloud-native environments
• Understanding of CI/CD, automation, or pipeline-based delivery
• Clear communication of technical decisions, tradeoffs, and ownership
• Able to operate in a CAC-enabled or secure environment
• Preferred Qualifications:
• Active TS/SCI clearance
• Active Secret clearance with eligibility for upgrade
• Familiarity with ML lifecycle tools such as MLflow, Kubeflow, Airflow, Argo, ClearML, or similar
• Background in model serving, inference APIs, or deploying ML systems in production
• Exposure to LLMs, transformer-based models, computer vision, NLP, or applied AI solutions
• Hands-on work with Kubernetes-based ML workloads
• Knowledge of observability and monitoring tools such as Prometheus, Grafana, or OpenTelemetry
• Experience in DoD, defense, intelligence, regulated, or mission-critical settings
• Work in edge, offline, air-gapped, low-bandwidth, D-DIL, or limited-compute environments

Qualifications

• U.S. citizenship

Skills

• Deployment of AI/ML systems
• CI/CD, automation, and pipeline-based delivery
• Kubernetes-based ML workloads
• Observability and monitoring tools
• Model serving, inference APIs, and deployment in production
• Edge, offline, air-gapped, low-bandwidth, D-DIL, or limited-compute environments

Benefits & Perks

• 100% covered certifications & training aligned to your role
• 401(k) with 100% match up to 6%
• Highly competitive PTO
• Comprehensive Medical, Dental, Vision coverage
• Life Insurance + Short & Long-Term Disability
• Home office & equipment plan
• Industry-leading weekly pay schedule