AI-ML Technical Delivery Manager

Role- AI-ML Delivery Manager

Experience-12+yrs

Job location - Chennai / Hyderabad

Mode of work- Hybrid

Role Summary :

The AI Manager leads the AI team, contributing to technical strategy and decision-making. This role involves managing the day-to-day delivery of the AI / ML team, responsible for building, deploying, and maintaining robust, scalable, and efficient ML models.

Essential Responsibilities :

• Provide technical leadership and strategic direction for the AI team, aligning with overall company objectives.
• 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).
• Contribute to AI / ML architecture design and implementation.
• 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.
• Serve as a primary point of contact for US & Thryve leaders, ensuring clear communication and aligning with project goals and objectives.
• 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.
• Manage day-to-day operations, ensuring adherence to process, scope, quality, and timelines.
• Assume the role of an engagement coordinator between the Thryve India team and US counterparts (techno-functional).
• Highlight risks and concerns to the leadership team and work with multiple stakeholders to establish alignment, contingency, and / or mitigation plans as required.
• Proactively track delivery statuses and project progress, taking ownership of all deliverables from Thryve.
• Communicate effectively with team members, US stakeholders, and management.
• 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.
• Propose and implement best engineering and research practices for scaling ML-powered features, enabling fast iteration and efficient experimentation with novel features.

Required Skills and Qualifications :