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

Dayton, OH

MLOps Engineer — AI/ML Systems Deployment
Location: Dayton, OH preferred
Work Arrangement: On-site preferred; remote may be considered for highly aligned, clearance-ready candidates able to support secure / CAC-enabled environments and travel as needed
Clearance: Active TS/SCI strongly preferred; active Secret may be considered for upgrade
Requirement: U.S. citizenship required

Build and Deploy Real-World AI Systems

Rackner is hiring an MLOps Engineer to move AI/ML systems from prototype → deployment → operational use in a secure, mission-focused environment.

This is not a research role—this is where models become reliable, repeatable, auditable systems that run in real-world conditions.

This role is ideal for engineers who want to:

  • Work across AI/ML, Kubernetes, infrastructure, and mission systems
  • Own deployed systems, not just experiments
  • Build high-demand MLOps expertise in secure and constrained environments
  • Deliver technology that is used, trusted, and operational

You will help operationalize AI/ML capabilities where reliability, performance, and trust matter most.

What You’ll Do

Operationalize AI/ML Systems

  • Deploy AI/ML models and ML-enabled applications into secure, real-world environments
  • Move workflows from experimentation into containerized, repeatable deployment pipelines
  • Support batch and real-time inference architectures
  • Bridge model development, software engineering, and platform operations

Own the ML Lifecycle

  • Build and operate production-grade ML pipelines
  • Support model versioning, lineage, reproducibility, and lifecycle governance
  • Work with tools such as MLflow, Kubeflow, Airflow, Argo, ClearML, or similar platforms

Build Cloud-Native ML Infrastructure

  • Deploy and support Kubernetes-based ML workloads
  • Containerize models, pipelines, and services using Docker or similar tools
  • Support CI/CD, automation, and repeatable deployment patterns for AI/ML systems

Engineer for Reliability

  • Monitor model and system performance after deployment
  • Support observability using tools such as Prometheus, Grafana, OpenTelemetry, or similar
  • Detect and resolve issues related to latency, reliability, drift, degradation, or resource usage

Support Secure and Constrained Environments

  • Help deploy AI/ML systems in secure, CAC-enabled, or constrained environments
  • Support limited compute, restricted data, degraded connectivity, and other operational constraints
  • Optimize systems for reliability and usability beyond ideal lab conditions

Create Repeatable Systems

  • Develop runbooks, deployment documentation, and operational playbooks
  • Build systems that can be understood, maintained, and operated by others

What You Bring

Core Experience

  • U.S. citizenship
  • Background in deploying ML systems, AI-enabled applications, or production software
  • Strong programming skills in Python
  • Hands-on work with Docker, containers, or containerized deployment
  • Familiarity with Kubernetes or cloud-native environments
  • Understanding of CI/CD, automation, or pipeline-based delivery
  • Clear communication of technical decisions, tradeoffs, and ownership
  • Ability to operate in a CAC-enabled or secure environment

Preferred Qualifications

  • Active TS/SCI clearance
  • Active Secret clearance with eligibility for upgrade
  • Familiarity with ML lifecycle tools such as MLflow, Kubeflow, Airflow, Argo, ClearML, or similar
  • Background in model serving, inference APIs, or deploying ML systems in production
  • Exposure to LLMs, transformer-based models, computer vision, NLP, or applied AI solutions
  • Hands-on work with Kubernetes-based ML workloads
  • Knowledge of observability and monitoring tools such as Prometheus, Grafana, or OpenTelemetry
  • Experience in DoD, defense, intelligence, regulated, or mission-critical settings
  • Work in edge, offline, air-gapped, low-bandwidth, D-DIL, or limited-compute environments

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

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 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.

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

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If you are an engineer who wants to move from building models or platforms to owning deployed AI/ML systems, we would like to connect.

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