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.

Experience
5-9 yrs