Lead MLOPs Engineer
nr ref: 115/12/2024/AJ/89273
W Antal zajmujemy się rekrutacją od ponad 20 lat. Dzięki działaniu w 10 wyspecjalizowanych dywizjach, świetnie orientujemy się w aktualnych trendach branżowych. Precyzyjnie określamy specyfikę stanowiska, klasyfikując kluczowe umiejętności i niezbędne kwalifikacje. Naszą misją jest nie tylko znalezienie kandydata, którego kompetencje wpisują się w wymagania danego ogłoszenia, ale przede wszystkim stanowiska, spełniającego oczekiwania kandydata. Numer rejestru agencji zatrudnienia: 496.
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.
