Role Overview :

As a Data Science Engineer, you will serve as a technical bridge between advanced machine learning research and scalable production environments. Your day-to-day involves architecting robust data pipelines, deploying sophisticated models on GCP, and refining MLOps workflows to ensure seamless model lifecycle management. You will collaborate closely with cross-functional teams, including data scientists, software engineers, and business stakeholders, to transform complex data sets into actionable intelligence. By bridging the gap between experimental AI and enterprise-grade infrastructure, you will directly influence business outcomes, optimize operational efficiency, and drive innovation that enhances the end-user experience across our global platforms.

Key Responsibilities :

- Architect and maintain scalable machine learning infrastructure on GCP to ensure high availability and performance for mission-critical AI applications.

- Implement end-to-end MLOps pipelines using Vertex AI to automate model training, evaluation, and deployment, thereby reducing time-to-market for new features.

- Develop and optimize high-performance Python-based algorithms to solve complex business problems and improve predictive accuracy for internal stakeholders.

- Collaborate with engineering teams to integrate machine learning models into existing software ecosystems, ensuring seamless interoperability and data integrity.

- Monitor model performance and data drift in production environments to proactively address technical challenges and maintain the reliability of AI-driven insights.

- Mentor junior engineers and contribute to technical documentation to foster a culture of engineering excellence and continuous improvement within the data science organization.

Required Skillset :

- Demonstrated expertise in building and deploying machine learning models within GCP environments, with a deep understanding of Vertex AI services and cloud-native architecture.

- Advanced proficiency in Python, with a proven ability to write clean, maintainable, and efficient code for large-scale data processing and model development.

- Strong command of MLOps best practices, including CI/CD for machine learning, model versioning, and automated monitoring, to ensure robust production systems.

- Exceptional analytical and problem-solving abilities, coupled with the capacity to communicate complex technical concepts to non-technical stakeholders effectively.

- Proven track record of working in high-growth, fast-paced environments, with the flexibility to thrive in remote or hybrid setups across multiple locations.

- Strong interpersonal skills, enabling effective collaboration within diverse, cross-functional teams to drive project alignment and business impact.

- A Masters degree or higher in Computer Science, Statistics, Mathematics, or a related quantitative field is preferred, reflecting a strong foundation in algorithmic thinking and data science principles.