Senior MLOps & ML Infrastructure Engineer

SimpliSafe; Boston (MA); Hybrid; USD 153,000 - 204,000; Full time

Benefits offered by this job

Free SimpliSafe system and monitoring

Employee Resource Groups (ERGs)

Job summary

SimpliSafe is looking for an experienced ML Infrastructure/MLOps Engineer to join as a Senior Machine Learning Engineer. You will drive development and deployment of ML models, optimize workflows, and ensure scalable, secure infrastructure.

Hybrid work model with a blend of office and home. You will lead ML model serving architectures, collaborate across teams, and implement robust monitoring, versioning, and deployment practices in AWS or other clouds.

Qualifications

• 5+ years in software/data engineering with 3+ years in MLOps or ML infra.
• Hands-on AWS knowledge including compute, networking, containers, observability.
• Experience with CI/CD pipelines, Docker, Kubernetes, and IaC tools (Terraform, CloudFormation).

Responsibilities

• Lead the architecture, deployment, and optimization of scalable ML model serving systems for real-time and batch use cases.
• Collaborate with data scientists, engineers, and stakeholders to operationalize ML models.
• Develop CI/CD pipelines for ML models enabling rapid, safe, and consistent model releases.
• Design, implement, and own production monitoring for ML models/systems.
• Manage cloud infrastructure, primarily in AWS or other major public clouds, to support ML workloads.
• Drive best practices in model versioning, observability, reproducibility, and deployment reliability.
• Serve in an on-call rotation as a first responder for software owned by your team.

Skills

AWS expertise

CI/CD pipelines

Docker

Kubernetes

Python

TensorFlow

PyTorch

Tools

Terraform

CloudFormation

Docker

Kubernetes

Job description

SimpliSafe is looking for an experienced ML Infrastructure/MLOps Engineer to join as a Senior Machine Learning Engineer. You will drive development and deployment of ML models, optimize workflows, and ensure scalable, secure infrastructure.

Hybrid work model with a blend of office and home. You will lead ML model serving architectures, collaborate across teams, and implement robust monitoring, versioning, and deployment practices in AWS or other clouds.