MLOPS Engineer

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