MLOps Engineer

Role Description

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.

What You’ll Do

• Own the ML Lifecycle (End-to-End)
• Build and operate production-grade ML pipelines
• Orchestrate workflows using Kubeflow, Airflow, or Argo
• Implement model versioning, lineage, and reproducibility standards
• Operationalize AI/ML Systems
• Deploy models into secure and constrained environments
• Transition workflows from experimentation → containerized pipelines → production systems
• Enable both batch and real-time inference architectures
• Engineer for Reliability
• 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
• Build Cloud-Native ML Infrastructure
• Deploy and manage Kubernetes-based ML workloads
• Containerize pipelines using Docker
• Support scalable training and inference workflows
• Establish Data Discipline
• Support feature engineering and dataset preparation
• Implement data versioning and governance practices (e.g., lakeFS)
• Apply metadata and data management standards
• Create Repeatable Systems
• Develop runbooks, playbooks, and documentation
• Build systems that are operationally sustainable and transferable

Qualifications

• 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

Mindset

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

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.

Benefits

• 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

Company Description

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.