Job Description

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 productionization of ML algorithms. Setup CI/CD/CT pipelines 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 Azure, 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 or Java preferred. Strong DevOps skills: 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 a plus, such as Beam, Spark, Pandas, SQL, Kafka, GCP Dataflow, etc. 3+ years of experience, including academic experience, in any of the above. Work Experience MS in Computer Science, Software Engineering, or equivalent field Experience with Cloud Platforms, especially GCP and Azure, 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 or Java preferred. Strong DevOps skills: 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 a plus, such as Beam, Spark, Pandas, SQL, Kafka, GCP Dataflow, etc. 3+ years of experience, including academic experience, in any of the above. Role & responsibilities