Senior AI Engineer

BloodFlow is building FlowCore, an AI-powered clinical decision support system designed to operate as an invisible layer of clinical intelligence inside hospital electronic health record systems.

FlowCore automatically reviews and contextualises the patient’s clinical information and delivers structured, evidence-based insights directly into the clinician’s existing workflow.

Our systems operate in highly regulated healthcare environments, where reliability, traceability, transparency and data security are essential. We are now looking for a Senior AI Engineer to help us build, deploy and scale FlowCore across hospitals and healthcare systems.

The role

We are looking for an experienced Senior AI Engineer who has built and deployed AI systems in production and understands what it takes to make them reliable, observable and maintainable.

You will work across AI engineering, LLMOps, quality assurance and infrastructure, helping us move models and AI pipelines from development into secure production environments.

A core part of the role will be designing systems that can be deployed on-premise within hospitals or through hybrid cloud architectures, while maintaining strong standards for privacy, auditability, performance and operational resilience.

This is not primarily a research role. We are looking for someone who understands the full lifecycle of production AI systems.

What you will do

• Design, build and maintain production-grade AI and SLM pipelines for FlowCore.
• Deploy and optimise language models and supporting services in on-premise and hybrid cloud environments.
• Build scalable inference architectures using GPUs, containers and model-serving frameworks.
• Define and implement LLMOps practices for model, prompt, dataset and pipeline versioning.
• Develop evaluation frameworks for measuring accuracy, reliability, consistency, safety and model behaviour.
• Implement observability, monitoring, logging and traceability across AI pipelines.
• Create mechanisms for reproducibility, rollback, audit trails and controlled releases.
• Design automated testing and quality assurance processes for probabilistic & deterministic AI systems.
• Monitor model performance, latency, resource utilisation and production degradation.
• Support the implementation of transparent and explainable AI systems, where outputs can be traced back to their underlying data, evidence and system version.
• Work closely with clinical, product, quality, regulatory and engineering teams.
• Contribute to technical decisions concerning infrastructure, security, privacy and deployment architecture.
• Help establish engineering standards for deploying AI safely in regulated healthcare environments.

What we are looking for

• Strong professional experience developing and deploying AI or machine learning systems in production.
• Experience operating AI, SLM & LLM systems beyond proof-of-concept stage.
• Strong understanding of LLMOps, MLOps and the lifecycle of production AI systems.
• Experience with model serving, containerisation and infrastructure orchestration.
• Experience deploying systems using Docker and technologies such as Kubernetes, vLLM, Triton Inference Server or similar platforms.
• Experience with GPU infrastructure, inference optimisation and production performance monitoring.
• Understanding of model, dataset, prompt and configuration versioning.
• Experience building automated evaluation and testing frameworks for AI systems.
• Knowledge of observability, experiment tracking, logging, model monitoring and incident investigation.
• Experience with on-premise, private cloud or hybrid cloud deployments.
• Strong understanding of secure software engineering, access control and data privacy.
• Proficiency in Python and experience building production APIs and backend services.
• Ability to translate complex AI systems into reliable, documented and auditable engineering processes.
• Strong ownership mentality and the ability to work in a fast-moving startup environment.

Particularly valuable experience

• Experience working in healthcare, medical devices or another regulated industry.
• Familiarity with quality management systems and software lifecycle documentation.
• Experience with AI validation, risk management or regulated software development.
• Experience with retrieval-augmented generation, knowledge graphs or evidence-grounded AI systems.
• Experience deploying open-source language models in private infrastructure.
• Experience with Azure, AWS or Google Cloud in hybrid deployment environments.

What success looks like

During your first months, you will help us:

• Strengthen the architecture of FlowCore’s production AI pipeline.
• Establish robust AI evaluation and release processes.
• Improve model and pipeline observability.
• Implement systematic versioning and traceability across the AI stack.
• Standardise how FlowCore is deployed within hospital infrastructure.
• Increase the reliability, reproducibility and auditability of every AI release.
• Work on a real clinical AI product being deployed with hospitals and healthcare partners.
• Help define how AI systems should be built for high-stakes, regulated environments.
• Take ownership of core technical and architectural decisions.
• Work directly with the founding, clinical, product and engineering teams.
• Contribute to a product designed to make clinical decisions faster, safer and better informed.
• Join the company at a stage where your work will have a direct impact on the product, engineering culture and technical direction.

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