Dev/ MLOps Engineer I (Full Stack)

Job Description

Dev/ MLOps Engineer I (Full Stack)

Location: HQ In Person

About Us:

Origin is redefining home security with TruShield Security, a hardware-free, router-based solution powered by our exclusive AI Sensing technology. TruShield goes beyond motion detection to deliver Verified Human Presence, accurately distinguishing between people and non-human activity. By leveraging existing WiFi networks and connected devices, it delivers awareness across the home without cameras, wearables, or additional hardware, eliminating false alarms and elevating protection.

Now part of ADT, Origin strengthens ADT’s promise of trusted protection by adding real-time awareness and context inside the home. When every second counts, knowing what is actually happening, not just that something happened, changes everything.

About the Job:

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.

What You’ll Do:

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

About You:

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 Have:

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

Full-time