Technical Lead - AI Enablement Squad at Discovery Limited

Key Purpose


The AI Enablement Squad Technical Lead is a senior hands‑on leader responsible for translating cutting‑edge data science into robust, scalable, production‑grade AI systems. The role provides deep technical leadership across the productionisation of machine learning and LLM solutions, with a strong emphasis on advanced Python engineering, sound software and systems architecture, and engineering excellence.
This role is accountable for technical design, implementation quality, and architectural integrity across AI-enabled systems, working closely with data scientists, engineers, and platform teams. The role acts as a technical authority and mentor, guiding engineering decisions, reviewing critical code, and supporting the growth of junior and mid-level technical team members.
The position involves hands-on ownership of the end-to-end technical lifecycle—from system and data pipeline design, through build, testing, deployment, and production monitoring. Success in this role requires architecting and implementing maintainable, resilient, and scalable systems, integrating effectively with existing enterprise platforms, and ensuring alignment with Group development, security, and governance standards.


Key outputs

The successful applicant will be responsible for but not limited to the following job functions:

Areas of responsibility may include but not limited to

Hands on Technical Leadership & Mentorship


Act as a senior hands-on technical lead and principal engineer, contributing directly to critical production Python codebases.
Provide technical mentorship and guidance to junior and mid-level engineers through code reviews, pairing, and architectural discussions.
Set and uphold high standards for code quality, testing, reliability, and maintainability, fostering a culture of engineering excellence.


Production Python & Systems Architecture


Lead the technical design and implementation of production-grade AI systems, with a strong emphasis on advanced Python engineering.
Design and influence system and application architecture for model deployment, inference services, data pipelines, and integrations.
Make pragmatic architectural decisions balancing scalability, resilience, performance, cost, and time-to-value, in alignment with Discovery's enterprise and security standards.


MLOps / LLMOps & Operational Excellence


Design, implement, and evolve production ML and LLM workflows, including deployment, monitoring, and lifecycle management.
Champion best practices in CI/CD, automated testing, observability, and incident analysis for AI-enabled systems.
Ensure AI systems are observable, resilient, and supportable in production environments.


Cross functional Technical Collaboration


Work closely with data scientists to translate experimental and research work into production-ready systems.
Collaborate with platform and infrastructure teams to ensure solutions integrate cleanly with existing enterprise platforms.
Provide clear technical input on feasibility, constraints, and trade-offs to business and technical stakeholders.


Technical Influence & Enablement


Influence the technical direction and sustainability of AI delivery across the organisation through shared patterns, tooling, and architectural guidance.
Drive the adoption of agreed engineering standards, platforms, and best practices for AI productionisation.
Represent the AI Enablement Squad as a technical authority in architecture and engineering forums, contributing to group-wide technical capability uplift.


Personal Attributes

The successful candidate would need to have the following competencies:


Collaborative mentor with a natural inclination to share knowledge.
Pragmatic and results-driven, focused on delivering robust solutions.
Intellectually curious with a passion for technology and innovation.
Excellent communicator, able to articulate complex technical ideas clearly.
Ownership mindset with resilience and adaptability.


Skills


Levels of proficiency in Python, SQL and cloud-native development to enable management of the delivery team
Similarly, for MLOps/LLMOps tools (e.g. MLflow, Kubeflow, LangChain, etc.), as well as CI/CD, containerisation (Docker, Kubernetes), and infrastructure-as-code
Advanced knowledge of cloud platforms (either GCP, AWS or Azure)
Agile delivery


Education and Experience

The following requirements are Essential:


Degree in Computer Science, Engineering, or related field
6+ years in software/data engineering or AI productionisation
8+ years, with leadership experience in cross-functional technical teams


The following requirements are advantageous:


Postgraduate qualification in AI, Data Science, or Systems Engineering