Applied AI Engineer Graduate

TrendAI™, the global AI security leader and enterprise business unit of Trend Micro, empowers organizations with full AI visibility and consolidated security that inspires confidence, drives innovation, and eliminates risk.
 
At TrendAI™, we’re always seeking exceptional talent; people who want to collaborate with the best and push boundaries together. Here, your work goes beyond building a career. You will help protect what matters and play a vital role in shaping a safer, more trustworthy AI-powered future.
 
AI Fearlessly.

OUR CULTURE:

We hire for capability and potential — not credentials alone. At TrendAI, you get real access: to senior people, to genuinely hard problems, and to work that actually moves things forward. You will have a voice, an opinion, and the freedom to use both and to be the best part of yourself.

JOINING TRENDAI ON ITS AI NATIVE JOURNEY:

TrendAI is on a deliberate path to become a fully AI Native company. This means rebuilding how we think, how we build, and how we operate, with AI at the foundation rather than bolted on at the edges.

If you join now, you are not walking into something already figured out — you are joining at the moment it is being built. The skills you develop here will make you genuinely formidable in one of the most in-demand areas in tech. The people who join at this stage will have had a hand in shaping what TrendAI becomes. That is a rare thing.

YOUR GRADUATE ROLE:

This is a 12-month contract for people who are serious about AI engineering and ready to make a real contribution from day one. You will join our Applied AI Engineering team and work on live internal products — building, shipping, and iterating on AI-native applications that change the way people across Trend Micro work.

You will be supported by experienced engineers, have access to our Career Success Support Programme, and join a team with a structured mentoring culture and a genuine commitment to your development. There may also be opportunities to travel to other European offices.

82% of our graduates over the last five years have gone on to secure a permanent role with us.

THE ROLE:

We build AI-native internal products — agentic workflows, intelligent retrieval systems, automation pipelines, and the platform layers that make them run reliably in production. You will own workstreams end-to-end: from shaping the problem through to design, build, evaluation, and deployment.

You will not be handed a spec and told to implement it. You will be expected to think, challenge, and contribute to how we build — not just what we build.

KEY RESPONSIBILITIES:

• Agentic AI & LLM engineering — Design and build agentic workflows and multi-agent systems. Implement tool-calling, state management, memory, and structured outputs using LangGraph and LangChain. Integrate frontier model APIs — Azure OpenAI, Anthropic, Gemini — in production contexts.
• RAG & retrieval systems — Build and optimise retrieval-augmented generation pipelines. Work with vector databases (pgvector, FAISS, Pinecone), embedding models, and retrieval evaluation. Debug grounding issues and model behaviour in production.
• Backend engineering & APIs — Build APIs and backend services that connect AI components to applications. Write clean, well-structured Python. Design for reliability and observability from the start, not as an afterthought.
• Tool & service integration — Connect AI agents to external services, databases, and internal APIs using tool-calling and MCP patterns. Understand how agents interact with the systems around them.
• Workflow orchestration — Build and maintain production-grade automation workflows. Design durable, observable processes that run reliably at scale.
• MLOps & production AI — Deploy and operationalise AI features. Implement monitoring, logging, and evaluation. Contribute to CI/CD pipelines and GitHub-based workflows using Docker and Azure.
• Frontend awareness — You do not need to be a frontend engineer, but you should be able to work across a full-stack context — understand how your backend AI components surface to end users, collaborate with frontend engineers, and contribute where needed using React or TypeScript.
• Research & evaluation — Stay current with the field. Evaluate emerging tools and frameworks. Contribute to proof-of-concept work and share findings across the team.
• Cross-functional collaboration — Translate requirements from business stakeholders into technical solutions. Communicate clearly to both technical and non-technical audiences.

TECHNOLOGY YOU WILL USE:

AI & orchestration - Python · LangGraph · LangChain · Azure OpenAI · Anthropic API · MCP

Data & retrieval - PostgreSQL / pgvector · FAISS · pandas · NumPy · SQL

Backend & infra - FastAPI · Docker · GitHub Actions · Azure ML · Azure Cloud

Frontend - React · TypeScript (working knowledge valued)

Dev tools - VS Code · GitHub Copilot · Claude Code · Git

Collaboration - Jira · Confluence · Microsoft Teams

WHAT WE ARE LOOKING FOR:

• A BSc or MSc in Artificial Intelligence, Computer Science, Data Science, Software Engineering, or a related STEM discipline — recently completed, or currently enrolled with a placement or industry year requirement. Strong engineering fundamentals matter more to us than the specific degree title.
• Solid Python — you write code that works, reads well, and can be reviewed by someone else without explanation.
• Understanding of core CS fundamentals: data structures, algorithms, and how software systems fit together.
• Practical exposure to LLMs and generative AI — a final year project, coursework, or personal experimentation. What matters is that you have engaged with it in practice, not just read about it.
• Familiarity with agentic patterns, RAG, vector databases, or frontier model APIs. You do not need production experience — but you should understand what these are and why they matter.
• Some awareness of full-stack engineering: what APIs are, how frontend and backend interact, and where AI components fit in that picture.
• Understanding of what production means: reliability, observability, not just functionality.
• Clear communicator — able to explain technical decisions to non-technical stakeholders and ask good questions when requirements are ambiguous.
• Self-motivated, collaborative, and intellectually honest about what you know and what you are still learning.
• A genuine interest in AI engineering as a craft — not just as a career label.