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

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

Location: Dayton, OH (On-site Preferred) | Remote Eligible (U.S.-based, Clearance-Ready)

Clearance: TS/SCI Preferred | Secret Eligible

Overview

Rackner is seeking an MLOps Engineer to support the deployment and lifecycle management of AI/ML systems within a secure, mission-focused environment.

This role is responsible for operationalizing machine learning capabilities—moving models from experimentation into reliable, deployable, and auditable systems.

You will work across:

machine learning

cloud-native infrastructure

distributed systems

…to ensure AI/ML systems are production-ready in environments where reliability, performance, and security are critical.

Responsibilities

Build and maintain production ML pipelines using tools such as Kubeflow, Airflow, or Argo

Deploy ML models into secure and constrained environments (including on-prem, air-gapped, or hybrid systems)

Implement model versioning, reproducibility, and lifecycle management (MLflow, ClearML)

Develop and operate containerized ML workloads using Docker and Kubernetes

Design and support model serving architectures (batch and real-time inference)

Monitor system and model performance using Prometheus, Grafana, OpenTelemetry

Support data preparation, feature engineering, and dataset versioning (lakeFS or similar)

Create technical documentation, runbooks, and operational standards

Collaborate with cross-functional teams to ensure successful integration into operational systems

Required Qualifications

U.S. Citizenship (required for clearance eligibility)

Experience deploying ML systems into production environments

Strong programming skills in Python

Experience with Kubernetes and containerized systems (Docker)

Hands-on experience with:

ML pipeline tools (Kubeflow, Airflow, Argo)

Model tracking/versioning tools (MLflow, ClearML)

Understanding of distributed systems and scalable architectures

Experience with cloud platforms (AWS, Azure, or GCP)

Preferred Qualifications

Active TS/SCI clearance

Experience with LLMs, transformer-based models, or computer vision systems

Familiarity with model serving frameworks and inference optimization

Experience working in regulated, defense, or mission-critical environments

Exposure to data versioning tools (lakeFS) and metadata standards

Experience supporting systems in air-gapped or secure 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.

What Sets This Role Apart

Work on AI/ML systems that are deployed and used in real-world environments

Build systems that prioritize reliability, reproducibility, and operational impact

Gain experience operating within secure, high-trust environments

Collaborate on modern MLOps, DevSecOps, and cloud-native architectures

About Rackner

Rackner is a software consultancy that builds cloud-native solutions for startups, enterprises, and the public sector. We specialize in:

cloud-native development

DevSecOps

AI/ML systems

distributed architecture

Our approach is cloud-first, cost-effective, and outcome-driven, delivering scalable and resilient systems.

Benefits

401(k) with 100% match up to 6%

Comprehensive Medical, Dental, Vision coverage

Life Insurance + Short & Long-Term Disability

Generous PTO

Weekly pay schedule

Home office & equipment support

Certification and training reimbursement

Apply

If you’re an engineer who wants to move from building models → owning production systems, we’d like to connect: https://grnh.se/71n3dndw5us

MLOps, Machine Learning Operations, Kubernetes, Docker, Kubeflow, MLflow, Airflow, Argo Workflows, Python, AI/ML, Model Deployment, Model Serving, DevSecOps, Cloud, TS/SCI, Clearance