Leading AI/ML Technical Deliveries

5 days ago

Erode, Tamil Nadu, India beBeeArtificial Full time US$ 1,80,000 - US$ 2,10,000
AI/ML Technical Delivery Manager Job Summary

The AI/ML Technical Delivery Manager plays a pivotal role in leading the AI team, contributing to technical strategy and decision-making. This position involves managing the day-to-day delivery of the AI/ML team, responsible for building, deploying, and maintaining robust, scalable, and efficient ML models.

Key Responsibilities:

  1. Provide technical leadership and strategic direction for the AI team, aligning with overall company objectives.
  2. Lead and mentor a team of data engineers, MLOps engineers, and machine learning engineers to achieve project objectives and deliverables (development and deployment of ML models for AI use cases across the platform).
  3. Contribute to AI/ML architecture design and implementation.
  4. Collaborate with business, engineering, infrastructure, and data science teams to translate their needs or challenges into production-grade Artificial Intelligence and Machine Learning models for batch and real-time requirements.
  5. Serve as a primary point of contact for US leaders, ensuring clear communication and aligning with project goals and objectives.
  6. Guide the AI/ML engineers with respect to business objectives, connecting the dots, and helping the team come up with optimal ML models based on use cases.
  7. Manage day-to-day operations, ensuring adherence to process, scope, quality, and timelines.
  8. Assume the role of an engagement coordinator between the India team and US counterparts (techno-functional).
  9. Highlight risks and concerns to the leadership team and work with multiple stakeholders to establish alignment, contingency, and/or mitigation plans as required.
  10. Proactively track delivery statuses and project progress, taking ownership of all deliverables from Thryve.
  11. Communicate effectively with team members, US stakeholders, and management.
  12. Stay up-to-date with the latest trends and advancements in AI and ML, and identify opportunities for the team to implement new models and technologies.
  13. Propose and implement best engineering and research practices for scaling ML-powered features, enabling fast iteration and efficient experimentation with novel features.

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