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

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

• Apply

• 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.