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
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.
This role is ideal for engineers who want to:
Work across AI/ML, Kubernetes, infrastructure, and mission systems
Own deployed systems, not just experiments
Build high-demand MLOps expertise in secure and constrained environments
Deliver technology that is used, trusted, and operational
You will help operationalize AI/ML capabilities where reliability, performance, and trust matter most.
What You’ll Do
Operationalize AI/ML Systems
Deploy AI/ML models and ML-enabled applications into 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
Build Cloud-Native ML Infrastructure
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
Support Secure and Constrained Environments
Help deploy AI/ML systems in secure, CAC-enabled, or constrained environments
Support limited compute, restricted data, degraded connectivity, and other operational constraints
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
What You Bring
Core Experience
U.S. citizenship
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
Ability 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
Clearance Requirements
Active TS/SCI clearance strongly preferred
Candidates with an active Secret clearance may be considered and supported for upgrade
Candidates without an active clearance must be:
U.S. citizens
eligible to obtain and maintain a clearance
able to work in a CAC-enabled or secure environment
Note: Start timelines and work scope may vary depending on clearance status and program requirements
Who We Are
Rackner is a software consultancy that builds cloud-native solutions for startups, enterprises, and the public sector. We are an energetic, growing team focused on solving complex problems through:
Distributed systems
DevSecOps
AI/ML
Cloud-native architecture
Our approach is cloud-first, cost-effective, and outcome-driven, delivering systems that scale and perform in real-world 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
Apply
If you are an engineer who wants to move from building models or platforms to owning deployed AI/ML systems, we would like to connect.