MLOpsDevOps Engineer

This is a remote position.

environment. We are looking for a highly motivated individual who can develop

cutting edge MLOps and DevOps frameworks to deploy AI models. The candidate

should have a solid grasp of stateoftheart cloud technologies best in class

deployment architectures/frameworks and production grade software. Finally the

role requires strong team and interdisciplinary collaboration to see products

through the development cycle from beginning to end.

Core Job Responsibilities:

Develop endtoend pipelines encompassing the ML lifecycle from data ingestion

data transformation model training &validation model deployment & 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 to cloud and onprem edge.

Manage a team of DevOps/MLOps engineers.

Learn and apply new tools technologies and industry best practices.

Requirements

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

preferred.

Strong DevOps skills: Linux/Unix environment testing troubleshooting

automation Git dependency management and build tools (GCP Cloud Build

Jenkins Bazel Gitlab CI/CD Github Actions etc.).

Data engineering skills a plus such as Beam Spark Pandas SQL Kafka GCP

Dataflow etc.

5 years of experience including academic experience in any of the above.

3 years of managing a DevOps/MLOps team.

Benefits

Best in the industry

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 preferred. Strong DevOps skills: Linux/Unix environment, testing, troubleshooting, automation, Git, dependency management, and build tools (GCP Cloud Build, Jenkins, Bazel, Gitlab CI/CD, Github Actions, etc.). Data engineering skills a plus, such as Beam, Spark, Pandas, SQL, Kafka, GCP Dataflow, etc. 5+ years of experience, including academic experience, in any of the above. 3+ years of managing a DevOps/MLOps team.