Technical Program Manager - AI/ML

Scope of Role

Key Responsibilities

• Backlog Management

• Own and maintain the shared engineering backlog, ensuring clear prioritization based on business goals and technical dependencies.
• Facilitate regular backlog grooming and sprint planning sessions across teams.

• Project Delivery

• Drive end-to-end delivery of projects, from requirements gathering to deployment, ensuring milestones are met.
• Track progress and ensure blockers are addressed promptly.

• Stakeholder Communication

• Serve as the primary liaison between engineering teams and business stakeholders (product owners, data scientists, researchers).
• Provide regular updates on project status, risks, and dependencies.

• Cross-Team Coordination

• Coordinate work across geographically distributed teams, ensuring alignment and effective collaboration.
• Manage time zone and cultural differences to optimize team performance.

• Process Improvement

• Identify and implement process improvements for delivery efficiency, quality, and team satisfaction.
• Foster a culture of continuous improvement and innovation.

• Risk Management

• Proactively identify delivery risks and mitigation strategies.
• Ensure compliance with data governance and security standards.

Key Regular Points of Contact on Engineering Team

• Head of Engineering
• Data Engineering Leads (Dublin, Krakow, Singapore, Bengaluru)
• AI Research Leads (Dublin, Krakow, Singapore, Bengaluru)
• Data Science Leads (Dublin, Krakow, Singapore, Bengaluru)
• Product Owners/Managers (relevant to each domain)

Data Engineering Fundamentals

• Good understanding of data architectures (data lakes, data warehouses, ETL/ELT pipelines).
• Familiarity with tools such as Spark, Airflow, Hadoop, or similar.
• Working knowledge of SQL and at least one scripting language (e.g., Python).

AI/Data Science Awareness

• Basic understanding of machine learning workflows and terminology.
• Awareness of common frameworks but not required to build models.
• Ability to communicate with AI/Data Science teams and understand their delivery needs.

Cloud Platforms

• Experience with at least one major cloud provider (AWS, Azure or GCP) and their data services.

DevOps & Automation

• Familiarity of CI/CD pipelines, containerization, and automated testing.
• Experience with source control (Git) and agile project management tools (Azure DevOps, Jira, Confluence).

Technical Communication

• Ability to read and discuss technical documentation, architecture diagrams, and project requirements.
• Comfortable facilitating discussions between technical and non-technical stakeholders.

Soft Skills Required

Proactive Problem Solver

• Takes ownership of issues and drives them to resolution.
• Anticipates risks and addresses them early.

Collaborative and Empathetic

• Builds trust and rapport across diverse, distributed teams.
• Listens actively and values input from all team members.

Adaptable and Resilient

• Thrives in a fast-paced, evolving environment.
• Flexible in approach and open to feedback.

Clear Communicator

• Communicates priorities, updates, and challenges clearly and constructively.
• Bridges gaps between technical and business stakeholders.

Continuous Learner

• Willing to learn new technologies and delivery practices as needed.
• Encourages a culture of improvement and innovation.

What Success Looks Like in the Role

• 
  Timely Delivery - Projects are delivered on time, within scope, and meet quality standards.
• Clear Prioritization - Backlogs are well-managed, with priorities clearly communicated and understood by all teams.
• Effective Collaboration - Teams across locations work seamlessly together, with minimal friction and strong alignment.
• Stakeholder Satisfaction - Stakeholders are regularly informed, and their needs are reflected in project outcomes.
• Process Excellence - Delivery processes are continuously improved, leading to increased efficiency and reduced bottlenecks.
• Risk Mitigation - Issues are identified early and resolved proactively, minimizing impact on delivery.
• Team Engagement - Engineering teams feel supported, empowered, and motivated to deliver high-quality work.