Lead MLOPs Engineer

Technology stack

•        GCP (must have!) , BigQuery, Cloud Storage, Apache Airflow, Cloud Composer,

·      Vertex AI, Dataproc, Compute Engine

•        CI/CD and Build tooling: Terraform, Terragrunt, Jenkins, Groovy, Crane, Kaniko

•        Python, PySpark, Docker, Jupyter, Apache Airflow, Spark, Java (optional, but would

·      be beneficial)

Key Responsibilities

•        Establish and maintain best practices for ML Ops. Including version control, CI/CD

•        pipelines and the Vertex Al Model Registry and End Points.

•        Implement MLOps tools to streamline model development, training, tuning,

•        deployment, monitoring and explain.

•        Deploy and Manage ML models on GCP's Vertex Al platform ensuring efficient and

•        scalable execution.

•        Identify and address performance bottleneck in ML models and pipelines.

•        Troubleshoot and resolve ML issues ensuring optimal model performance and

•        costs. Work Closely with Compliance Analytics data scientists to prepare and

•        preprocess data for model training and evaluation.

•        Assist in feature engineering and selection to ensure model performance

•        Develop techniques to visualize and explain model behavior ensuring model transparency and accountability in-line with PRA S51/23 guidelines.

•        Collaborate with infrastructure and DevOps teams to establish efficient deployment and scaling strategies.

Pipeline Development:

•        Build and maintain robust pipelines for model training, tuning and deployment leveraging components of Vertex Al and GCP tooling like Cloud Composer utilizing Python and Java and Big Query.

•        Implement automated monitoring and alerting to track model performance and identify potential issues.

•        Develop and maintain data quality checks and validation including reconciliations in-line with Data Quality and Retention Controls.

•        Implement robust security measures to protect sensitive data and models.

Required Skills and Experience:

•        Strong proficiency in ML Ops principles and tools.

•        Proficiency in data engineering and pipeline development.

•        Experience with GCP including Big Query, Cloud Composer and Vertex Al.

•        Strong problem-solving and analytical skills.

•        Strong proficiency in Python

•        Experience with Java would be beneficial.