• Bachelor s in Computer Science, Engineering, or related field (Master s preferred).
• Experience in software engineering, or ML/Data platform development.
• Expertise is preferred in Python, Java, and/or PySpark for distributed data and service development.
• Proven experience architecting cloud-native data and ML systems ( Google Cloud Platform +Databricks).
• Deep understanding of system design, data modeling, and distributed computing.
• Demonstrated leadership in scaling large, data-intensive systems and mentoring engineering teams.
• Excellent communication, technical leadership, and stakeholder management skills.
• Strong system design and architecture skills.
• Excellent debugging and troubleshooting abilities.
• Expertise with automated testing.
• Ability to thrive in a highly dynamic, fast-paced environment.
Essential Functions & Key Responsibilities:
• Define and implement the data architecture and software systems that underpin our ML and AI platforms.
• Lead design and development of scalable data pipelines, APIs, and services enabling Data-as-a-Service for ML use cases.
• Architect real-time and batch data serving frameworks for training, inference, and feedback loops.
• Drive engineering excellence and platform scalability across distributed environments.
• Collaborate with AI Platform leadership, MLOps, and backend teams to shape long-term technical strategy.
• Mentor and guide senior engineers, establishing standards for design, testing, and deployment.
• Evaluate emerging technologies and tools to strengthen the platform s reliability and performance.
• Champion best practices in software architecture, data quality, and performance optimization
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