MLOps Engineer

Glassbeam's mission is to enable smarter hospitals and labs with ML-powered products. Our products enable better medical device management, maintenance, and troubleshooting.
As a startup, we provide a positive work environment. No bureaucracy. No useless meetings. Clear goals and clarity of purpose. Spend your day building something useful and get satisfaction from work. We empower you to take ownership. You will be encouraged to come up with and try new ideas. Get a sense of achievement from delivering innovative products and playing an important role in the growth and success of a startup.
If you are an experienced MLOps engineer who enjoys working in a startup environment and delivering
innovative products that customers use daily, we would love to hear from you.
Role
Productionize ML models. Manage model lifecycle. Build ML-powered SaaS products running on AWS, Azure,
and other clouds.

The ideal candidate:
• Has experience building production-grade ML training/ inference pipelines for a SaaS company
• Takes ownership of the goals assigned and gets things done
• Strives to develop scalable, maintainable, and reliable software
Responsibilities
• Develop and maintain ML training pipeline
• Develop and maintain ML inference pipeline
• Develop tools for ML model deployment and lifecycle management
• Develop tools for managing ML pipeline/models
• Develop tools for monitoring ML pipeline/models
• Develop plans for both pipeline and model verification/validation
• Develop tools for ensuring robustness/correctness of ML pipelines/models under all scenarios
Requirements
Minimum
• 5+ years’ experience developing and deploying in production scalable and reliable SaaS products
• 3+ experience developing micro-services architecture-based SaaS products
• 3+ years’ experience building production-grade SaaS products using Scala
• 1+ years’ work experience with Spark, HDFS and Kafka
• Strong design and programming skills
• Demonstrated adherence to writing unit tests
• Experience developing software using SCRUM methodology
Plus
• Hands-on experience with Cassandra, Play framework, Vertica, Delta Lake
• Work experience with Dockers, Kubernetes, Kubeflow, Airflow, Dagster
• Experience deploying production-grade ML software on AWS, Azure, Google Cloud
• Experience building web apps using Angular or React