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.

2. 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.

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.

Education & background

• 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 experience delivering production systems.

AI engineering skills

• Practical experience building AI‑enabled applications, not just prototypes (LLM‑based features, recommendation systems, classification, ranking, search, etc.).
• Hands‑on experience with at least one major cloud AI / LLM platform (e.g. Azure OpenAI, Google AI) and associated SDKs/APIs.
• Solid understanding of:
  • Different solution patterns (LLM, classical ML, heuristic/rule‑based, search/retrieval) and when to use each.
  • Prompt and system design, RAG, evaluation and testing of AI behaviours.

Software & platform engineering

• Strong programming skills in Python and TypeScript/JavaScript, including building production‑grade services (not just notebooks).
• Experience integrating services into NodeJS backends (NestJS, Next.js, Express, Apollo Server) and React frontends.
• Good understanding of REST and GraphQL APIs, microservices, and event‑driven patterns.
• Experience with Git (e.g. GitHub), CI/CD pipelines, and automated testing.
• Experience with Azure (preferred) and/or Google Cloud, including identity, networking, and security basics.
• Working knowledge of containerisation (Docker) and orchestration (Kubernetes or similar) and Infrastructure as Code (e.g. Terraform).

Data & observability

• Familiarity with modern data stacks, ideally including Snowflake and dbt.
• Experience implementing logging, metrics, and tracing to understand and improve system and AI behaviour in production.

Mindset & behaviours

• Pragmatic problem solver: able to choose between AI, traditional engineering, or a hybrid approach based on impact, risk, and complexity.
• Strong product mindset and user focus.
• Clear communicator who can explain trade‑offs to both technical and non‑technical stakeholders.

Nice to have

• Experience in education / digital learning or other content‑centric domains.
• Experience with LLMOps / ML platforms (model registries, feature stores, evaluation frameworks, prompt/version management, guardrails).
• Background in high‑availability, mission‑critical systems and cost optimisation for cloud workloads.
• Experience shaping engineering standards or internal platforms for broader adoption.

Key relationships

• Executive: Director of AI / Head of AI Centre of Excellence, Transformation Sponsor, AI Steering Group.
• Functional partners: Legal, Security, Procurement, HR, Finance, ILPT, OpCo Leadership and AI Specialists.
• Day‑to‑day collaboration: AI Engineering Lead, Enablement Lead, Data Governance Lead, Data Analytics Lead, OpCo AI Specialists, Product Managers, Engineering teams, Transformation/TMO.