- Minimum 2 years of hands-on experience as a MLOps engineer.
- Experience with Cloud settings (AWS, GCP or Microsoft).
- Familiarity with the usage of MLOps tools throughout the entire ML life cycle (AI monitoring, model serving, experiment tracking, etc.).
- Experience with Spark (Yarn/Kubernetes cluster management - An advantage).
- DevOps experience - Big advantage.
- SageMaker/Vertex/Azure ML experience - Big advantage.
- Customer facing experience/PM background - Big advantage.
- This position combines both the regular MLOps engineer work, of designing and developing TRSTai's ML framework from scratch, as well as transforming TRSTai's clients requirements to our product features and designing and adapting our product to fit each client unique setup (on prem, VPC, different ML pipelines, etc.), allowing a huge diversity of ever-changing technological challenges.
- As an early team member in an MLOps oriented company, you will have significant impact on the technological directions that the company will take.