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

Role Overview

Rackner is seeking an MLOps Engineer to deploy and manage the full lifecycle of production-grade AI/ML systems in a secure, mission-focused environment. This is not a research role—models must become reliable, deployable, and auditable.

Responsibilities

• Own the ML lifecycle (end-to-end)
  • Build and operate production ML pipelines
  • Orchestrate workflows with Kubeflow, Airflow, or Argo
  • Implement model versioning, lineage, and reproducibility standards
• Operationalize AI/ML systems
  • Deploy models into secure, constrained environments
  • Move from experimentation to containerized pipelines and production systems
  • Support batch and real-time inference architectures
• Engineer for reliability
  • Ensure reproducibility, auditability, stability
  • Monitor model performance and system health using Prometheus, Grafana, and OpenTelemetry
  • Detect and resolve issues like model drift and system degradation
• Build cloud-native ML infrastructure
  • Deploy and manage Kubernetes-based ML workloads
  • Containerize pipelines with Docker
  • Support scalable training and inference workflows
• Establish data discipline
  • Feature engineering and dataset preparation
  • Data versioning/governance (e.g., lakeFS)
  • Apply metadata and data management standards
• Create repeatable systems
  • Produce runbooks, playbooks, and documentation for operational sustainability

Requirements

• Strong programming skills in Python
• Experience deploying ML systems into production environments
• Hands-on experience with:
  • ML pipeline orchestration tools: Kubeflow, Airflow, or Argo
  • Experiment tracking: MLflow or ClearML
• Infrastructure & systems:
  • Kubernetes and containerized systems (Docker)
  • Familiarity with CI/CD pipelines
  • Understanding of distributed systems and scalable architectures
• ML application exposure (deployment/integration focus):
  • LLMs / transformer-based models and/or
  • Computer vision systems (e.g., YOLO, Faster R-CNN)
• Reliability-first mindset and ability to operate in complex, evolving environments

Clearance Requirements

• Active TS/SCI clearance strongly preferred
• Secret clearance candidates may be considered and supported for upgrade
• Non-cleared candidates must be U.S. citizens eligible to obtain/maintain clearance and able to work in a CAC-enabled/secure environment

Why This Role

• Build production systems rather than prototypes
• Work across ML, infrastructure, and deployment pipelines
• Develop high-demand MLOps expertise in constrained, high-trust environments