AI Data Team Lead

Fetcherr is revolutionizing industries through the power of deep learning and predictive AI. At the heart of our innovation is the Large Market Model (LMM), a sophisticated, adaptable AI engine that forecasts market dynamics with unparalleled precision. We empower our partners with real-time, data-driven intelligence to navigate complex markets. Join us to build the future of dynamic, AI-driven commerce.

We are seeking an exceptional AI Data Team Lead to guide and scale the development and operational excellence of our Large Market Model (LMM). You will be a player-coach, combining deep technical ownership of the core AI systems with the responsibility of mentoring and leading a team of talented AI Data Engineers. You will set the technical vision for data and modeling pipelines, ensuring they are architected, engineered, and maintained to transform raw information into predictive intelligence at scale, all while instilling rigorous software development and MLOps practices across the team.

What You'll Do (Key Responsibilities):

• Team Leadership & Mentorship: Lead, mentor, and grow a team of AI Data Engineers. Set technical standards, drive project planning, manage team priorities, and foster a culture of technical excellence and ownership.
• Strategic Pipeline Architecture: Define the long-term architectural roadmap for robust, scalable data pipelines that ingest, integrate, and fuel our LMM. Oversee the successful execution of this roadmap by the team.
• Advanced Feature Strategy: Direct the team's efforts in conceptualizing and implementing sophisticated feature engineering strategies, collaborating closely with Data Scientists and domain experts to translate complex market dynamics into predictive signals.
• Drive Innovation: Champion continuous research and experimentation with new technologies in data engineering, distributed computing, MLOps, and model optimization to maintain the LMM's competitive edge.
  
  

• A proactive, ownership-driven mindset with excellent problem-solving, communication, and people leadership skills.
• A B.Sc. or Master's degree in Computer Science, Engineering, Mathematics, or a related quantitative field.
• A minimum of 5 years of commercial experience building production-level AI data pipelines systems, with at least 3 years in a technical lead or team management capacity.
• Expert-level proficiency in Python and its data science/machine learning ecosystem (e.g., Pandas, Scikit-learn).
• Proven experience designing, maintaining, and overseeing scalable data processing pipelines using distributed computing frameworks (Dask, Spark etc.).
• Expert-level, hands-on experience with MLOps principles and tools, including CI/CD for machine learning and pipeline orchestrators (e.g., Dagster, Airflow).
• Extensive working experience with SQL-based ETL pipelines to extract, transform, and load large-scale datasets from diverse sources.
• Strong software engineering fundamentals, including experience with containerization (Docker), cloud platforms (AWS, GCP, or Azure), and leading API development initiatives.
  
  

• Experience in the aviation or a related high-frequency, dynamic pricing industry.
• Active contributions to open-source projects in the data or ML space.