The ideal candidate possesses a unique blend of system-wide vision and coding ability. You won't just monitor performance; you will define the standards for deployment, leverage internal ML infrastructure to solve complex gaps, and ensure our AI translates into flawless real-world performance. We are looking for an initiator who can bridge the gap between high-level research and production-grade execution.
Responsibilities:
Define Deployment Standards: Determine the criteria for model success, ensuring every release meets the high bar for both technical precision and business logic.
Drive Cross-Functional Orchestration: Serve as the primary technical interface between Research, Product, Engineering, and Data teams.
Optimize the System: Use your coding ability and internal ML infrastructure to creatively improve the "Recognition Factory."
Champion Performance: Proactively identify production performance gaps and improve model behavior to better serve specific client use cases.
Evolve Infrastructure: Leverage and improve internal tools to automate the model lifecycle and ensure data integrity at scale.
Professional Experience: At least 3 years of experience in a relevant technical environment (e.g., Data, Research, or Engineering units).
System-Wide Perspective: Ability to understand complex architectures and how model changes impact the entire solution and the end-user experience.
Proactive Ownership: A natural initiator who identifies gaps before they become issues and proposes creative solutions to close them.
Technical Foundation: BSc in Computer Science, Mathematics, or Engineering (MSc is an advantage).
Coding Ability: Good programming skills (Python is preferred) with the ability to automate analytical processes and interface with internal ML infrastructure.
Analytical Skills: Strong experience in model analysis and deep learning concepts. You should be comfortable driving value and identifying patterns within the data.
Experience in System Engineering: Previous experience in system engineering or managing complex technical workflows is a strong advantage.
Data Proficiency: Familiarity with MySQL, BigQuery, MongoDB, or PostgreSQL.
Communication: Excellent ability to communicate technical ML concepts to diverse stakeholders across the business.
Production Mindset: Enthusiastic about working in a high-growth, fast-paced production environment.
