Graduate AI Engineer

Job Role

We are hiring a Graduate AI Engineer to help build a reusable agentic AI platform and a series of related agentic workflow systems. You will work with senior AI engineers and business stakeholders to implement Python components, prepare domain packs, generate synthetic training and evaluation data, validate structured model outputs, build small backend services, and support experiments with LLMs and small domain-specific models. This role suits a recent graduate who wants to learn how real AI products are built: beyond notebooks and chatbot demos, into tested, observable, auditable systems that can support business workflows safely. The graduate will also get involved across several agentic AI system builds, learning how reusable platform components become practical workflow products for different business domains.

Key Responsibilities

• Implement platform components: Build well‑scoped Python modules for pack loading, validation, data generation, evaluation and reporting.
• Support domain‑pack development: Help convert business knowledge into structured intents, tools, policies, seed cases, eval cases and operating prompts.
• Build testable AI workflows: Use simple graph/workflow patterns, structured outputs and validation rules to support safe next‑action proposals.
• Create evaluation assets: Prepare golden datasets, test cases, regression checks and review reports that show when the model is behaving correctly.
• Improve reliability: Add unit tests, integration tests, error handling, logging and simple observability for AI/data pipelines.
• Document clearly: Write concise technical notes, diagrams, README updates and handover material for reusable ISx4 assets.
• Learn production AI practice: Develop understanding of prompt injection, PII boundaries, audit trails, human‑in‑the‑loop controls, model cost and latency.
• Collaborate with the team: Work AI & Analytics team to turn platform milestones and related agentic system builds into shipped software increments.

Key Requirements

• Strong Python fundamentals: functions, classes, typing basics, virtual environments, packages and debugging.
• Ability to write readable code and tests using pytest or comparable testing tools.
• Comfort with Git, GitHub, pull requests and working from issues or implementation briefs.
• Basic backend/API understanding: HTTP, JSON, REST APIs and environment variables.
• Familiarity with SQL or structured data handling in Python.
• Ability to work with YAML/JSON configuration, schemas and validation logic.
• Some exposure to AI/ML/LLMs through coursework, dissertation, internship or self‑directed projects.
• Clear written communication and willingness to document assumptions, limitations and next steps.