Senior DevOps Engineer (Infrastructure & MLOps)

Prompt

23h•Hybrid

About The Position

Prompt is revolutionizing healthcare by delivering highly automated and modern B2B enterprise software to rehab therapy businesses, their teams, and the patients they serve. We have established ourselves as the go-to platform in the space, setting a new standard in healthcare technology and rapidly growing our market share. We’re constantly identifying new challenges across outpatient therapy and the broader healthcare space. As a Senior DevOps Engineer at Prompt Therapy, you will play a crucial role in managing and improving our infrastructure and deployment process, ensuring scalability, security, and reliability. As we continue to innovate, you will also lead the charge in building and scaling the infrastructure that powers and monitors our AI-driven features. If you're a proactive problem solver with a deep understanding of cloud environments, infrastructure automation, and MLOps workflows, we want you on our team!

Requirements

5+ years of experience in a DevOps or infrastructure role.
Expert knowledge of cloud platforms such as AWS, GCP and Azure
Strong experience with containerization technologies (Docker, ECS / Kubernetes).
Proven track record of designing and managing complex CI/CD pipelines.
Experience with MLOps workflows (model versioning, retraining pipelines, or feature stores).
Hands-on experience with monitoring and logging tools (Datadog, Prometheus, Grafana, MLflow).
Expertise in scripting languages (Python is a must, along with Bash, Go, etc.).
Proficiency with infrastructure automation tools (Terraform, Ansible, or CloudFormation).
Excellent communication skills and the ability to bridge the gap between traditional DevOps and Data Science teams.

Nice To Haves

Specific experience in AWS Lambda, SageMaker and other related AI/ML services.
Experience with database management (SQL/NoSQL) and data pipeline orchestration, including Google BigQuery
Knowledge of security best practices specifically regarding data privacy in AI and healthcare (HIPAA compliance).
Previous experience in fast-paced, agile startup environments.

Responsibilities

Design, implement, and manage highly available infrastructure for our cloud-based platforms (AWS).
Work with our internal engineering teams to architect and support AI/ML infrastructure, specifically managing AWS Lambda infrastructure, and some legacy SageMaker environments for model training, hosting, and inference.
Create and automate robust deployment pipelines using CI/CD tools (GitLab / GitHub Actions) for both web applications and machine learning models.
Build, maintain, and scale containerized applications with Docker and ECS/Fargate.
Implement MLOps best practices to streamline the transition of models from development to production.
Ensure system scalability and reliability through proactive monitoring, logging, and automated alerting.
Collaborate with both Product Engineers and Data Scientists to optimize performance, security, and infrastructure costs.
Manage and evolve our Infrastructure as Code (IaC) footprint.