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

At Rackner, we build systems where advanced technologies move beyond prototypes and into real-world operational use.

We are seeking an MLOps Engineer to support the deployment and lifecycle management of AI/ML systems within a secure, mission-focused environment.

This is not a research role.

This is where models become reliable, deployable, and auditable systems.

You will operate at the intersection of:

• machine learning
• cloud-native infrastructure
• distributed systems

…and ensure AI/ML systems are production-ready in environments where reliability and performance matter.

• Build and operate production-grade ML pipelines
• Orchestrate workflows using Kubeflow, Airflow, or Argo
• Implement model versioning, lineage, and reproducibility standards

• Deploy models into secure and constrained environments
  Transition workflows from experimentation → containerized pipelines → production systems
  Enable both batch and real-time inference architectures

• Design systems for reproducibility, auditability, and stability
• Monitor model performance and system health using Prometheus, Grafana, OpenTelemetry
• Detect and resolve issues such as model drift and system degradation

• Deploy and manage Kubernetes-based ML workloads
• Containerize pipelines using Docker
• Support scalable training and inference workflows

• Support feature engineering and dataset preparation
• Implement data versioning and governance practices (e.g., lakeFS)
• Apply metadata and data management standards

• Develop runbooks, playbooks, and documentation
• Build systems that are operationally sustainable and transferable

• Experience deploying ML systems into production environments
• Strong programming skills in Python
• Hands-on experience with:
  
  • ML pipeline tools (Kubeflow, Airflow, Argo)
  • Experiment tracking tools (MLflow, ClearML)

• Experience with Kubernetes and containerized systems (Docker)
• Familiarity with CI/CD pipelines
• Understanding of distributed systems and scalable architectures

• Experience working with:
  
  • LLMs or transformer-based models
  • Computer vision systems (YOLO, Faster R-CNN)
• Focus on deployment and integration, not pure research

• Systems thinker who prioritizes reliability over novelty
• Comfortable operating in complex, evolving environments
• Focused on delivering real-world outcomes

• 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

This role is a career accelerator for engineers who want to:

• Move beyond experimentation and own production systems
• Work across ML, infrastructure, and deployment pipelines
• Build in high-trust, secure environments
• Develop high-demand MLOps expertise in constrained systems
• Deliver systems that are used, not just built

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.

If you’re an engineer who wants to move from building models → owning production systems, we’d like to connect.

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