MLOps Engineer — AI/ML Systems & Deployment (TS/SCI Preferred)
Location: Dayton, OH (On-site Preferred) | Remote Eligible (U.S.-based, Clearance-Ready)
Clearance: TS/SCI Preferred | Secret Eligible
Overview
Rackner is seeking an MLOps Engineer to support the deployment and lifecycle management of AI/ML systems within a secure, mission-focused environment.
This role is responsible for operationalizing machine learning capabilities—moving models from experimentation into reliable, deployable, and auditable systems.
You will work across:
machine learning
cloud-native infrastructure
distributed systems
…to ensure AI/ML systems are production-ready in environments where reliability, performance, and security are critical.
Responsibilities
Build and maintain production ML pipelines using tools such as Kubeflow, Airflow, or Argo
Deploy ML models into secure and constrained environments (including on-prem, air-gapped, or hybrid systems)
Implement model versioning, reproducibility, and lifecycle management (MLflow, ClearML)
Develop and operate containerized ML workloads using Docker and Kubernetes
Design and support model serving architectures (batch and real-time inference)
Monitor system and model performance using Prometheus, Grafana, OpenTelemetry
Support data preparation, feature engineering, and dataset versioning (lakeFS or similar)
Create technical documentation, runbooks, and operational standards
Collaborate with cross-functional teams to ensure successful integration into operational systems
Required Qualifications
U.S. Citizenship (required for clearance eligibility)
Experience deploying ML systems into production environments
Strong programming skills in Python
Experience with Kubernetes and containerized systems (Docker)
Hands-on experience with:
ML pipeline tools (Kubeflow, Airflow, Argo)
Model tracking/versioning tools (MLflow, ClearML)
Understanding of distributed systems and scalable architectures
Experience with cloud platforms (AWS, Azure, or GCP)
Preferred Qualifications
Active TS/SCI clearance
Experience with LLMs, transformer-based models, or computer vision systems
Familiarity with model serving frameworks and inference optimization
Experience working in regulated, defense, or mission-critical environments
Exposure to data versioning tools (lakeFS) and metadata standards
Experience supporting systems in air-gapped or secure 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.
What Sets This Role Apart
Work on AI/ML systems that are deployed and used in real-world environments
Build systems that prioritize reliability, reproducibility, and operational impact
Gain experience operating within secure, high-trust environments
Collaborate on modern MLOps, DevSecOps, and cloud-native architectures
About Rackner
Rackner is a software consultancy that builds cloud-native solutions for startups, enterprises, and the public sector. We specialize in:
cloud-native development
DevSecOps
AI/ML systems
distributed architecture
Our approach is cloud-first, cost-effective, and outcome-driven, delivering scalable and resilient systems.
Benefits
401(k) with 100% match up to 6%
Comprehensive Medical, Dental, Vision coverage
Life Insurance + Short & Long-Term Disability
Generous PTO
Weekly pay schedule
Home office & equipment support
Certification and training reimbursement
Apply
If you’re an engineer who wants to move from building models → owning production systems, we’d like to connect: https://grnh.se/71n3dndw5us
MLOps, Machine Learning Operations, Kubernetes, Docker, Kubeflow, MLflow, Airflow, Argo Workflows, Python, AI/ML, Model Deployment, Model Serving, DevSecOps, Cloud, TS/SCI, Clearance