AI Tech Lead

Global; City Of London; On-site; GBP 80,000 - 100,000; Full time

Job summary

A leading media company in City of London seeks an AI Tech Lead to drive the technical enablement of AI across its platforms. The successful candidate will design scalable APIs, integrate AI-driven solutions, and collaborate with various teams to enhance user experiences. A Bachelor's degree in Computer Science and proficiency in Python are required for this role, along with strong problem-solving skills and experience with AWS and data engineering.

Qualifications

• Proficiency in Python and SQL.
• Hands-on experience with AWS services and data warehousing.
• Familiarity with AI technologies and generative AI tools.

Responsibilities

• Design, build, and maintain robust, scalable APIs for stakeholders.
• Co-develop and deploy AI-driven solutions and features.
• Ensure stability, performance, and cost efficiency of systems.

Skills

Proficiency in Python

API development with FastAPI

Data engineering fundamentals

Strong problem-solving focus

Excellent collaboration skills

Education

Bachelor's degree in Computer Science or related field

Tools

AWS services

Snowflake

OpenAI

AssemblyAI

GraphQL with Strawberry

Job description

Accepting applications until:
28 November 2025

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.

As an AI Tech Lead at Global, you will:

Key Responsibilities:

• 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 GraphQL services with Strawberry where appropriate.

• AI and Machine Learning Integration (40%): Co-develop and deploy AI-driven solutions (MVPs/POCs and production), leveraging OpenAI, AssemblyAI, 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.

What You'll Love About This Role:

• 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.

What Success Looks Like:

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.

What You'll Need:

• 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 GraphQL is a plus.

• Hands-on experience with AWS services and modern data warehousing (Snowflake).

• Familiarity with AI technologies and generative AI tools, including OpenAI, AssemblyAI, 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.