Senior Machine Learning Engineer

Requirements:

• Be able to collaborate with the DS team for model deployment.
• Strong hands-on experience in Python coding, PySpark, and SQL.
• Has working experience in GCP Airflow to deploy/hostmodels in a data proc cluster and run inference on batch data stored in BQ.
• Knows how to create DAGs using different types of operators available.
• Good understanding of version control systems.
• Experience in batch model deployment.
• Able to automate repetitive tasks and ensure seamless deployment with minimal manual intervention.
• Good written and verbal communication.
• Goodunderstanding ofend-to-endML life cycle.
• Hands-on experience with Google cloud-BigQuery, Dataproc
• Strong working experience in Python and PySpark to build production-ready ML applications.
• Good at identifying production issues with ML models and pipelines and collaborating with Data Scientists for resolution.
• Understanding of ML terminology and its role in optimising the model.
• Onboard the models for performance and stability metrics to display them in Dashboard UI.
• Knowledge of ML services and deployment strategies on GCP and AWS.
• Produce build and deployment automation scripts to integrate between services
• Good Understanding of Version control systems (Gitlab) and working around it.
• Experience working with Batch Model pipelines.
• Must have experience in GCP services - Cloud Run, BigQuery, Vertex AI, Cloud Storage, Cloud SQL
• Collaborate with DS and DE to get the data issues solved.
• Good Written and Verbal Communication Skills.
• Technical Skillset: ML Lifecycle, MLOPs, Python, SQL, Pyspark, Airflow, Jenkins, Docker, GitLab, CI/CD, GCP Services: Data Proc, Cloud Run, BigQuery, Vertex AI, Cloud Storage, Cloud SQL.