Dev/ MLOps Engineer I (Full Stack)

ORIGIN WIRELESS, INC.•Rockville, MD

4h•Hybrid

About The Position

We are looking for a Dev / MLOps Engineer I to build and support the infrastructure that powers our cloud applications and machine learning systems. In this role, you will work across the stack, contributing to frontend and backend services while building the DevOps and MLOps foundations that enable scalable, reliable, and automated product development. You will partner closely with product, engineering, and research teams to support CI/CD pipelines, cloud infrastructure, and machine learning workflows. This role is ideal for an early-career engineer who is interested in full-stack development and wants to grow into DevOps and MLOps while working on real-world AI systems.

Requirements

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field (Master’s preferred)
0–3 years of experience in software engineering, DevOps, or MLOps
Strong programming skills in Python, with experience in scripting (Bash) and SQL
Familiarity with CI/CD tools such as GitHub Actions, Jenkins, or similar
Experience working with Docker and containerized environments (Kubernetes is a plus)
Exposure to cloud platforms, especially AWS (EC2, S3, Lambda, etc.)
Understanding of data pipelines and machine learning workflows
Strong problem-solving skills and ability to work in a fast-paced, collaborative environment
Excellent communication and interpersonal skills

Nice To Haves

Experience with ML platforms such as SageMaker or similar tools
Familiarity with workflow orchestration tools (Airflow, Kubeflow)
Exposure to monitoring tools (CloudWatch, Prometheus, Grafana)
Experience with NoSQL databases or time-series data systems
Exposure to JavaScript/TypeScript or backend API development
Experience with IoT, sensor data, or distributed systems

Responsibilities

Build, maintain, and optimize CI/CD pipelines to support application and ML workflows
Develop and deploy containerized services using Docker and Kubernetes
Support cloud infrastructure across AWS (preferred), with exposure to Azure or GCP
Implement infrastructure-as-code using tools such as Terraform or CloudFormation
Contribute to the development of frontend interfaces and backend services for internal tools
Build and maintain data and ML pipelines, including data ingestion, validation, training, and deployment
Support model lifecycle management, including versioning, tracking, and performance monitoring
Implement monitoring, logging, and alerting to ensure system reliability and observability
Conduct load and stress testing to evaluate performance and scalability of systems
Debug issues across the stack, including applications, infrastructure, and ML pipelines
Collaborate with research teams to support model development and productionization workflows
Drive automation to reduce manual processes across DevOps and ML systems
Build and support infrastructure that powers AI systems deployed at scale
Gain hands-on experience across full-stack development, DevOps, and MLOps environments
Collaborate cross-functionally with product, research, and engineering teams to deliver production-ready systems