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
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Translate business and product requirements into concrete AI solution designs, assessing when AI is appropriate and what type (LLM, classical ML, search, rules, hybrid).
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Design, implement, and maintain AI services and components that can be integrated into Infinitas products and internal workflows.
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Ensure solutions are reliable, testable, observable, secure, and cost‑effective.
2. Build reusable AI capabilities & APIs
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Develop reusable AI building blocks (libraries, APIs, services, templates) that product teams can plug into:
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NodeJS / TypeScript backends (NestJS, Next.js, Express, Apollo Server).
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React frontends and REST/GraphQL APIs.
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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
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Implement LLM-powered features where appropriate (e.g. content support, feedback, summarisation, assistance for teachers and learners).
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Use patterns such as retrieval-augmented generation (RAG), prompt and system design, and tool/function calling when they add value.
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Combine LLMs with other techniques (search, rules, ML models, analytics) to build robust end‑to‑end solutions.
4. Data, grounding & evaluation
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Work with data and content teams to define grounding strategies (knowledge bases, embeddings, vector search, Snowflake/dbt pipelines).
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Contribute to data pipelines and feature flows that support AI use cases, ensuring quality and traceability.
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Define and implement evaluation and testing for AI components (quality, safety, fairness, performance), including automated tests and golden datasets.
5. Platform, MLOps & engineering practices
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Contribute to the AI platform and tooling used by data scientists, ML engineers, and product teams (environments, registries, experiment tracking, CI/CD).
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Use containerisation and orchestration (e.g. Docker, Kubernetes) and Infrastructure as Code (e.g. Terraform) to deploy and manage AI services in Azure.
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Apply and champion modern engineering practices: TDD where appropriate, CI/CD, code review, observability, automation, and Kanban.
6. Security, safety & governance
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Embed security, privacy, and safety controls into AI solutions (access control, logging, guardrails, policy checks).
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Work with Legal, Security, and Data Governance to align implementations with regulatory and policy requirements.
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Help shape and apply AI design and usage guidelines across the organisation.
7. Collaboration & ways of working
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Partner with:
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AI Engineering Lead, Enablement Lead, Data Governance Lead, Data Analytics Lead
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OpCo AI Specialists, Product Managers, engineering teams (NodeJS/React)
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Legal, Security, Procurement, HR, Finance, ILPT, Transformation/TMO
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Support product teams in discovery and delivery phases: from exploring solution options to landing production implementations.
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Share patterns, examples, and reusable components to raise the overall AI engineering maturity.
Education & background
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Bachelor’s or Master’s degree in Computer Science, Software Engineering, AI/ML, or related field, or equivalent practical experience.
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5+ years in software or platform engineering, ML engineering, or MLOps, including experience delivering production systems.
AI engineering skills
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Practical experience building AI‑enabled applications, not just prototypes (LLM‑based features, recommendation systems, classification, ranking, search, etc.).
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Hands‑on experience with at least one major cloud AI / LLM platform (e.g. Azure OpenAI, Google AI) and associated SDKs/APIs.
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Solid understanding of:
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Different solution patterns (LLM, classical ML, heuristic/rule‑based, search/retrieval) and when to use each.
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Prompt and system design, RAG, evaluation and testing of AI behaviours.
Software & platform engineering
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Strong programming skills in Python and TypeScript/JavaScript, including building production‑grade services (not just notebooks).
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Experience integrating services into NodeJS backends (NestJS, Next.js, Express, Apollo Server) and React frontends.
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Good understanding of REST and GraphQL APIs, microservices, and event‑driven patterns.
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Experience with Git (e.g. GitHub), CI/CD pipelines, and automated testing.
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Experience with Azure (preferred) and/or Google Cloud, including identity, networking, and security basics.
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Working knowledge of containerisation (Docker) and orchestration (Kubernetes or similar) and Infrastructure as Code (e.g. Terraform).
Data & observability
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Familiarity with modern data stacks, ideally including Snowflake and dbt.
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Experience implementing logging, metrics, and tracing to understand and improve system and AI behaviour in production.
Mindset & behaviours
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Pragmatic problem solver: able to choose between AI, traditional engineering, or a hybrid approach based on impact, risk, and complexity.
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Strong product mindset and user focus.
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Clear communicator who can explain trade‑offs to both technical and non‑technical stakeholders.
Nice to have
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Experience in education / digital learning or other content‑centric domains.
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Experience with LLMOps / ML platforms (model registries, feature stores, evaluation frameworks, prompt/version management, guardrails).
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Background in high‑availability, mission‑critical systems and cost optimisation for cloud workloads.
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Experience shaping engineering standards or internal platforms for broader adoption.
Key relationships
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Executive: Director of AI / Head of AI Centre of Excellence, Transformation Sponsor, AI Steering Group.
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Functional partners: Legal, Security, Procurement, HR, Finance, ILPT, OpCo Leadership and AI Specialists.
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Day‑to‑day collaboration: AI Engineering Lead, Enablement Lead, Data Governance Lead, Data Analytics Lead, OpCo AI Specialists, Product Managers, Engineering teams, Transformation/TMO.
