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/Type Script, React, Snowflake/dbt, Terraform, CI/CD).

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
  .

2. Build reusable AI capabilities & APIs

• Develop reusable AI building blocks (libraries, APIs, services, templates) that product teams can plug into:

  • NodeJS / Type Script 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.

3. 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
  .

4. 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.

5. 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.

6. 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.

7. 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
  .

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, AI/ML, or related field
  , or equivalent practical experience.

• 5+ years in software or platform engineering
  , ML engineering
  , or MLOps
  , including…