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 builtWho We AreRackner 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
