AI Engineer – AI Engineering & Platforms (AI Centre of Excellence)

Role purpose

Infinitas Learning is building a modern AI Centre of Excellence to power the next generation of digital learning products. As an AI Engineer – AI Engineering & Platforms , you will design, build, and operate the AI capabilities, services, and platforms that product and data teams use to solve real business problems.

Your core focus is AI engineering : turning ideas into robust, secure, and maintainable solutions. Sometimes this will mean building LLM-based workflows or agents ; in other cases, the right answer may be classical ML , search and retrieval, rule-based logic, or well-designed analytics and automation. You will help teams choose and implement the right approach, not force everything into a single pattern.

You will work on top of our Azure-hosted products , while also leveraging Google AI capabilities where they make sense, and integrating with our existing stacks (NodeJS/TypeScript, React, Snowflake/dbt, Terraform, CI/CD).

Key responsibilities

1.End‑to‑end AI solution engineering

• Translate business and product requirements into concrete AI solution designs , assessing when AI is appropriate and what type (LLM, classical ML, search, rules, hybrid).
• Design, implement, and maintain AI services and components that can be integrated into Infinitas products and internal workflows.
• Ensure solutions are reliable, testable, observable, secure, and cost‑effective .

1. Build reusable AI capabilities & APIs

• Develop reusable AI building blocks (libraries, APIs, services, templates) that product teams can plug into:
• NodeJS / TypeScript backends (NestJS, Next.js, Express, Apollo Server).
• React frontends and REST/GraphQL APIs .
• Abstract different providers (e.g. Azure OpenAI, Google AI , internal models) behind stable interfaces so teams can adopt AI without deep platform knowledge.

1. Applied AI & LLM engineering

• Implement LLM-powered features where appropriate (e.g. content support, feedback, summarisation, assistance for teachers and learners).
• Use patterns such as retrieval-augmented generation (RAG) , prompt and system design, and tool/function calling when they add value.
• Combine LLMs with other techniques (search, rules, ML models, analytics) to build robust end‑to‑end solutions .

1. Data, grounding & evaluation

• Work with data and content teams to define grounding strategies (knowledge bases, embeddings, vector search, Snowflake/dbt pipelines).
• Contribute to data pipelines and feature flows that support AI use cases, ensuring quality and traceability.
• Define and implement evaluation and testing for AI components (quality, safety, fairness, performance), including automated tests and golden datasets.

1. Platform, MLOps & engineering practices

• Contribute to the AI platform and tooling used by data scientists, ML engineers, and product teams (environments, registries, experiment tracking, CI/CD).
• Use containerisation and orchestration (e.g. Docker, Kubernetes) and Infrastructure as Code (e.g. Terraform) to deploy and manage AI services in Azure.
• Apply and champion modern engineering practices : TDD where appropriate, CI/CD, code review, observability, automation, and Kanban.

1. Security, safety & governance

• Embed security, privacy, and safety controls into AI solutions (access control, logging, guardrails, policy checks).
• Work with Legal, Security, and Data Governance to align implementations with regulatory and policy requirements .
• Help shape and apply AI design and usage guidelines across the organisation.

1. Collaboration & ways of working

• Partner with:
• AI Engineering Lead, Enablement Lead, Data Governance Lead, Data Analytics Lead
• OpCo AI Specialists, Product Managers, engineering teams (NodeJS/React)
• Legal, Security, Procurement, HR, Finance, ILPT, Transformation/TMO
• Support product teams in discovery and delivery phases: from exploring solution options to landing production implementations.
• Share patterns, examples, and reusable components to raise the overall AI engineering maturity .