AI Platform Engineer

AI Platform Engineer

(LLM Ops & Scalable ML)

Location — Remote-first within the EU/EEA (occasional EU travel for team off-sites)
Department — Engineering
Employment type — Full-time, permanent
 

About Trialize

Trialize is transforming clinical trials with an AI-driven SaaS platform that automates study set-up, streamlines data flow and boosts data integrity. We serve pharmaceutical companies, biotech firms and CROs, helping them run faster, more reliable trials and bring life-changing therapies to patients sooner.

Role overview

We are looking for a AI Platform Engineer who can design, build and operate the next generation of our LLM-powered infrastructure.

Must-have experience

• Proven success with MCP, A2A and LoRA (or other parameter-efficient fine-tuning methods) in production.
• Demonstrably fast thinker / fast maker – you can prototype, benchmark and ship in days.
• Pro-level coding in Python or TypeScript / JavaScript, including test automation and CI/CD.
• Expertise in graph databases (schema design, Cypher/Gremlin, sharding, backup, HA).
• Deep knowledge of async messaging patterns with Kafka, gRPC or tRPC.
• Hands-on production experience with Kubernetes, Terraform and multi-cloud deployment.
• Ability to stand up end-to-end solutions (similar to Lovable, Rork, or equivalents) autonomously.
• Expert understanding of Retrieval-Augmented Generation (RAG) design patterns, latency trade-offs and evaluation metrics.

Nice-to-have

• Familiarity with GPU scheduling (Karpenter, Kubeflow, Ray Serve).
• Prior work on FDA-regulated or ISO-compliant software.
• Contributions to open-source LLM ops or vector-database projects.

Why join Trialize?

• Build the future of clinical AI – your work shortens the path to new medicines.
• Autonomy & speed – ship without red tape in a senior, high-trust team.
• Continuous learning – budget for conferences, certs and cloud credits.
• Remote flexibility – work where you are most productive, meet the team quarterly.

How to apply

Attach your CV (PDF) and a brief cover letter.
In your letter, tell us — in fewer than 100 words each:

1. The fastest ML prototype you ever shipped (timeline, stack, impact).
2. How you used LoRA or a similar method to slash training cost or inference latency.
3. Your favourite RAG architecture and why you chose it.

We review applications on a rolling basis and aim to respond within ten working days.