Compunnel, Inc.
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.
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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.
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