Overview
We are recruting for one of the worlds leading MedTech and Health Information management organisations, enabling better, smarter, and safer healthcare to improve lives. A long legacy of delivering breakthrough solutions, solving some of healthcare’s most complex challenges through the responsible use of data, technology, and clinical insight.
Building mission‑critical health information systems that support clinicians, healthcare providers, and patients worldwide. This work sits at the intersection of healthcare delivery, advanced data systems, and applied AI, ensuring that innovation is paired with trust, safety, and real‑world usability.
The Impact You’ll Make in this Role
As a Principal ML Engineer, you will lead the technical architecture and engineering strategy for integrating sophisticated AI into high‑stakes Healthcare Information Systems (HIS). We are looking for a seasoned builder who prioritises reliability, system performance, security, and automated scalability over hype or experimentation for its own sake.
While many roles focus on the “science” of modelling, your mission is the engineering of the ecosystem. You will architect the robust MLOps pipelines, platforms, and cloud infrastructure required to move models from experimental notebooks into mission‑critical clinical environments. You will act as the bridge between raw healthcare data and resilient, production‑grade AI services.
Responsibilities
Production Lifecycle: Lead the design and implementation of end‑to‑end ML lifecycles, with a strong focus on automated CI/CD pipelines, model versioning (e.g. MLflow, DVC), and reproducible experimentation.
Inference at Scale: Architect high‑performance serving layers for both LLMs and classical ML models, ensuring low latency, high availability, and security in a regulated healthcare cloud environment.
Agentic Orchestration: Build and operate the infrastructure for agent‑based reasoning systems, ensuring these workflows are traceable, auditable, and fully integrated into existing healthcare information systems.
Data Reliability: Design robust data pipelines (ETL/ELT) to process healthcare‑specific formats (FHIR, HL7, DICOM) into high‑quality features for both real‑time and batch inference.
Hybrid Infrastructure: Manage and optimise cloud‑native infrastructure across AWS, Azure, and GCP using Infrastructure as Code (Terraform or Pulumi) to support compute‑intensive ML workloads.
System Integrity: Implement comprehensive monitoring and observability frameworks to detect data drift, model degradation, and system bottlenecks before they impact clinical outcomes.
Technical Leadership & Governance
Engineering Authority: Act as the lead architect for the ML platform, ensuring systems meet healthcare security and compliance requirements (e.g. HIPAA, HITRUST) and follow security‑by‑design principles.
Operational Excellence: Establish rigorous standards for code quality, containerisation (Docker, Kubernetes), system documentation, and production readiness across the engineering organisation.
Strategic Mentorship: Foster a culture of “ML as Engineering” by guiding and mentoring engineers to build maintainable, modular, and scalable production systems.
Your Skills and Expertise
To be successful in this role, you will bring:
Bachelor’s degree or higher in Computer Science, Software Engineering, or a related technical field.
10+ years of software engineering experience, with at least 6 years deploying and maintaining large‑scale ML systems in production (beyond research or proof‑of‑concept work).
Core Technical Requirements
MLOps & Cloud: Expert‑level experience with cloud platforms (AWS, GCP, Azure) and orchestration tools such as Kubernetes, Kubeflow, or Airflow.
Engineering & Programming: Expert‑level Python and Java or Go (or similar), with strong knowledge of backend frameworks and distributed system design patterns.
Data Engineering: Strong experience with Spark, Snowflake, Databricks, and designing scalable data and feature platforms.
Applied AI: Hands‑on experience deploying Generative AI (LLMs) and agentic frameworks (LangChain, LangGraph) within containerised microservices architectures.
Additional Experience That Will Help You Succeed
GPU optimisation, quantisation, or advanced model‑serving frameworks (e.g. vLLM, TGI).
Master’s or PhD in Computer Science, Software Engineering, or a related field.
Deep understanding of security and compliance in regulated industries such as healthcare, finance, or defence.
Proven ability to design distributed systems handling high concurrency and large‑scale data processing.
Hybrid role based in Dublin, Ireland
Travel: Up to 10% domestic
Eligibility: Must be legally authorised to work in the country of employment without visa sponsorship
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