Description

You will build and manage end-to-end machine learning pipelines and productionize algorithms. You own the full ML lifecycle, from data ingestion and transformation to training, validation, serving, and evaluation.

Responsibilities

• Collaborate with AI scientists to move algorithms into production environments.
• Set up CI/CD/CT pipelines and model repositories for ML algorithms.
• Deploy models as a service across cloud and on-premise infrastructure.
• Implement new tools and industry best practices for ML engineering.

Required Skills

• 5+ years of experience in machine learning engineering or relevant academic research.
• Proficiency in Python.
• Experience with GCP and cloud-native tools including Docker and Kubernetes.
• Knowledge of edge computing.
• Familiarity with orchestration tools such as MLflow, Kubeflow, Airflow, Vertex AI, or Azure ML.
• Strong command of Linux/Unix environments.
• Experience with testing, troubleshooting, and automation.
• Proficiency with Git, dependency management, and build tools like GCP Cloud Build, Jenkins, GitLab CI/CD, or GitHub Actions.

Preferred Skills

• Data engineering experience with Beam, Spark, Pandas, SQL, Kafka, or GCP Dataflow.

Key Skills

Education

Any Graduate