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

Описание вакансии

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

Dayton, OH (On-site Preferred) | Remote Eligible (CAC-Ready Candidates)

Mission Environment | AI/ML Infrastructure | National Security Impact

About the Role

At Rackner, we are building the operational backbone that turns AI/ML capability into real-world mission outcomes. We are seeking an MLOps Engineer to own the lifecycle of AI/ML systems—from experimentation to deployment—within a mission-critical, classified environment supporting Air Force and NASIC-aligned programs.

This is not a research role; This is where models become reliable, deployable, auditable systems.

You will operate at the intersection of:

• Machine learning
• Distributed systems
• Cloud-native infrastructure

…and ensure that AI/ML systems work in the environments where failure is not an option.

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 mission environments (including constrained or classified systems)
• Transition workflows from Jupyter experimentation → containerized pipelines → production systems
• Enable both batch and real-time inference architectures

Engineer for Reliability, Not Just Performance

• Design systems for reproducibility, auditability, and stability
• Implement monitoring for:

+ model performance & drift

+ system health & latency

• Use tools like Prometheus, Grafana, and OpenTelemetry

Build Cloud-Native ML Infrastructure

• Deploy and manage Kubernetes-based ML workloads
• Containerize pipelines using Docker / OCI standards
• Scale compute for training and inference workloads

Establish Data Discipline

• Enable data versioning and governance (lakeFS or similar)
• Support feature engineering and dataset preparation pipelines
• Apply metadata standards (e.g., STAC) where applicable

Create Repeatable Systems

• Develop runbooks, playbooks, and deployment standards
• Build systems that can be operated by others; not just understood by you

What You Bring

Core Experience

• Experience deploying ML systems into production environments
• Strong background in Python and ML frameworks (PyTorch, TensorFlow, etc.)
• Hands-on experience with:

+ ML pipeline orchestration tools (Kubeflow, Airflow, Argo)

+ Experiment tracking (MLflow, ClearML)

Infrastructure & Systems

• Experience with Kubernetes and containerized workloads
• Familiarity with CI/CD for ML systems
• Understanding of distributed systems and scalable architectures

ML Application Exposure

• 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 values reliability over novelty
• Comfortable operating in ambiguous, high-stakes environments
• Able to translate experimental work into operational capability

Why This Role Matters (What You Get)

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

• Move beyond experimentation

+ Own systems that actually get deployed and used

• Operate at the systems level

+ Work across ML, infrastructure, and mission integration

• Build in high-trust environments

+ Where correctness, auditability, and reliability matter

• Develop rare, high-demand expertise

+ MLOps in constrained / classified environments is a differentiated skillset

Shape how AI is operationalized—not just built

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 consultancy with a passion for solving big problems across industries.

We enable digital transformation through:

• Distributed systems
• DevSecOps
• AI/ML
• Cloud-native architecture

Our approach is cloud-first, cost-effective, and outcome-driven—focused on delivering real capability, not just code.

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’re an engineer who wants to move from building models → owning systems, we want to talk.

#MLOps #MachineLearning #Kubernetes #AIEngineering #CloudNative #DevSecOps #ArtificialIntelligence #DataEngineering #DefenseTech #NationalSecurity #AIInfrastructure #Hiring #TechCareers