AI Tech Lead

Accepting applications until: 30 January 2026 Job Description Your Role: AI Tech Lead

You’ll lead the technical enablement of AI across Global’s Audio & Digital and Out-of-Home platforms—turning ideas into reliable, scalable products. You’ll set the architecture for APIs, data flows, and AI services, and mentor engineers while partnering with product, editorial, ad ops, and commercial tech.

• API and Data Product Development (40%): Design, build, and maintain robust, scalable APIs for internal and external stakeholders using FastAPI; define data requirements, build reliable pipelines, and ensure data accuracy, availability, and scalability on AWS and Snowflake; contribute to new features and platform enhancements, including Graph
  
  QL services with Strawberry where appropriate.

• AI and Machine Learning Integration (40%): Co‑develop and deploy AI‑driven solutions (MVPs/POCs and production), leveraging OpenAI, Assembly
  
  AI, and other commercial LLM APIs; implement generative AI features that enhance user experiences and personalization; continually evaluate and improve AI tools by integrating new data points, adding features, and establishing evaluation and monitoring.

• Technical Support and Collaboration (20%): Ensure stability, performance, and cost efficiency of data and AI systems; troubleshoot production issues and resolve data/system incidents; work closely with cross‑functional teams to align technical solutions to business goals, compliance, and user needs.

• Think Big: Shape Global’s AI platform and reusable components that power content, ad tech, and OOH operations.

• Own It: Lead architecture, reviews, and hands‑on coding for critical services and AI integrations.

• Keep it Simple: Create clear APIs, schemas, and documentation that make AI easy to adopt.

• Better Together: Partner with product, studios, sales, and ops to turn AI into measurable outcomes.

In your first few months, you’ll have:

• Delivered an initial set of secure APIs and data pipelines supporting a live AI use case.
• Shipped at least one AI MVP/POC (e.g., copy approval, content tagging, or assistant tooling) with baseline evaluation metrics.
• Implemented observability and cost dashboards for API and AI workloads.
• Established coding standards, review rituals, and a lightweight evaluation framework for prompts/models.

• Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent experience.
• Proficiency in Python and SQL; strong API development with FastAPI (or similar); experience with Strawberry for Graph
  
  QL is a plus.
• Hands‑on experience with AWS services and modern data warehousing (Snowflake).
• Familiarity with AI technologies and generative AI tools, including OpenAI, Assembly
  
  AI, and other commercial LLM APIs.
• Solid data engineering fundamentals: pipelines, testing, versioning, observability, and performance tuning.
• Strong problem‑solving focus on scalability, usability, and user experience.
• Excellent collaboration and communication skills for diverse technical and non‑technical audiences.