We are seeking a Data Science Engineer to join a specialized project team focused on building production-level data pipelines and enabling scalable machine learning workflows.
The ideal candidate will have strong expertise in Python, GCP, CI/CD, Docker/Kubernetes, and Vertex AI, and will collaborate closely with Data Scientists and ML Engineers in a fast-paced environment.
Key Responsibilities
- Design, develop, and maintain data pipelines for machine learning models using Python and Vertex AI.
- Collaborate with Data Scientists and ML Engineers to operationalize propensity models and LLM-based solutions.
- Implement cloud solutions using GCP or AWS, ensuring scalability, security, and compliance.
- Build and manage CI/CD pipelines for automated deployments and testing.
- Containerize applications using Docker and orchestrate with Kubernetes.
- Monitor data integrity, security, and performance across cloud environments.
- Create and manage API endpoints for model integration and data services.
- Utilize Q Flow for Vertex AI to streamline ML workflows and pipeline execution.
- Ensure production-level code quality and adherence to software development best practices.
Required Qualifications
- Minimum 5 years of experience in Data Engineering or a related field.
- Strong programming skills in Python, with experience in relevant libraries and frameworks.
- Hands-on experience with CI/CD pipelines and automation tools.
- Proficiency in containerization using Docker and Kubernetes.
- Solid understanding of GCP (preferred) or similar cloud platforms (AWS/Azure).
- Familiarity with Vertex AI and ML pipeline orchestration.
- Knowledge of data security and monitoring in cloud environments.
- Excellent communication and collaboration skills for cross-functional teamwork.
Preferred Qualifications
- Experience with Q Flow and advanced ML workflow orchestration.
- Exposure to LLM-based solutions and production-level AI systems.