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Skills:

Core Job Responsibilities:

Develop end-to-end ML pipelines encompassing the ML lifecycle from data ingestion, data transformation, model training, model validation, model serving, and model evaluation over time.
Collaborate closely with AI scientists to accelerate product ionization of ML algorithms.
Setup CI/CD/CT pipelines, model repository for ML algorithms
Deploy models as a service both on-cloud and on-prem.
Learn and apply new tools, technologies, and industry best practices.

Key Qualifications:

MS in Computer Science, Software Engineering, or equivalent field
Experience with Cloud Platforms, especially GCP and related skills: Docker, Kubernetes, edge computing
Familiarity with task orchestration tools such as MLflow, Kubeflow, Airflow, Vertex AI, Azure ML, etc.
Fluency in at least one general purpose programming language. Python - Required
Strong skills in the following: Linux/Unix environment, testing, troubleshooting, automation, Git, dependency management, and build tools (GCP Cloud Build, Jenkins, Gitlab CI/CD, Github Actions, etc.).
Data engineering skills are a plus, such as Beam, Spark, Pandas, SQL, Kafka, GCP Dataflow, etc.
5+ years of experience, including academic experience, in any of the above.