MLOps Engineer

BloodFlow

Lisboa

Presencial

EUR 50 000 - 70 000

Tempo integral

14 dias+

Recebe mais respostas dos empregadores

Envia um currículo específico para a oferta em poucos minutos.

Vantagens oferecidas por esta oferta de emprego

Competitive compensation and benefits package

Professional development and training opportunities

Equity participation

Resumo da oferta

A healthcare AI company in Lisbon is seeking an MLOps Engineer to build and maintain ML infrastructure and ensure reliable performance for their AI systems. The role emphasizes deploying AI models in production and ongoing performance monitoring. Candidates should have substantial experience with tools like Kubernetes and Docker, and a passion for advancing healthcare technology through AI. Competitive compensation and benefits are offered.

Qualificações

3+ years experience in DevOps, MLOps, or production ML systems.
Strong expertise in Kubernetes, Docker, and cloud platforms.
Experience with CI/CD pipelines, monitoring tools, and infrastructure as code.
Proficiency in Python and familiarity with ML frameworks (PyTorch, TensorFlow).
Understanding of model deployment patterns and microservices architecture.
Experience with version control, testing, and automation.
Experience in healthcare technology or regulated environments.
Knowledge of MLOps tools like MLflow, Kubeflow, or similar platforms.
Understanding of GDPR, HIPAA, or medical device compliance.
Experience with vector databases and LLM deployment.
Background in site reliability engineering (SRE).

Responsabilidades

Build and maintain ML infrastructure using Kubernetes, Docker, and cloud services.
Implement CI/CD pipelines for AI model deployment and updates.
Design scalable architectures for both cloud and on-premise deployments.
Manage model versioning, rollbacks, and A/B testing infrastructure.
Implement comprehensive monitoring for model performance and drift detection.
Build dashboards and alerting systems for production AI systems.
Track key metrics: latency, accuracy, throughput, and resource utilization.
Ensure compliance with healthcare audit and traceability requirements.
Implement security best practices for AI model deployment.
Ensure GDPR compliance and data privacy in ML pipelines.
Support regulatory requirements for medical device certification.
Manage secure data handling and model access controls.

Conhecimentos

DevOps

MLOps

Kubernetes

Docker

Cloud platforms

Python

ML frameworks

CI/CD pipelines

Monitoring tools

Infrastructure as code

Healthcare technology

Regulatory environments

GDPR

HIPAA

Vector databases

Site reliability engineering

Descrição da oferta de emprego

Build and maintain ML infrastructure, deploy AI models in production, and ensure reliable performance monitoring for our healthcare AI platform.

Ready to Apply?

Join our growing team of talented individuals

Everything you need to know about this role at BloodFlow

About BloodFlow

At BloodFlow, we're building an AI platform that interprets blood test results in their full clinical context — helping doctors make faster, safer, and more informed decisions.

We combine LLMs, RAG pipelines, and medical best practices to transform raw lab data into structured, actionable insights. Our solution is already being used by clinics and hospitals, and we're preparing for our first regulatory certifications and CE marking as a Class IIa medical device.

Role Summary

As MLOps Engineer, you'll build and maintain the infrastructure that powers our AI models in production. You'll ensure our AI systems are reliable, scalable, and meet the high standards required for healthcare applications.

This is a hands‑on technical role perfect for someone who loves building robust systems and has a passion for making AI work reliably in critical environments.

Responsibilities

Build and maintain ML infrastructure using Kubernetes, Docker, and cloud services
Implement CI/CD pipelines for AI model deployment and updates
Design scalable architectures for both cloud and on‑premise deployments
Manage model versioning, rollbacks, and A/B testing infrastructure
Implement comprehensive monitoring for model performance and drift detection
Build dashboards and alerting systems for production AI systems
Track key metrics: latency, accuracy, throughput, and resource utilization
Ensure compliance with healthcare audit and traceability requirements
Implement security best practices for AI model deployment
Ensure GDPR compliance and data privacy in ML pipelines
Support regulatory requirements for medical device certification
Manage secure data handling and model access controls

What We’re Looking For

3+ years experience in DevOps, MLOps, or production ML systems
Strong expertise in Kubernetes, Docker, and cloud platforms
Experience with CI/CD pipelines, monitoring tools, and infrastructure as code
Proficiency in Python and familiarity with ML frameworks (PyTorch, TensorFlow)
Understanding of model deployment patterns and microservices architecture
Experience with version control, testing, and automation
Experience in healthcare technology or regulated environments
Knowledge of MLOps tools like MLflow, Kubeflow, or similar platforms
Understanding of GDPR, HIPAA, or medical device compliance
Experience with vector databases and LLM deployment
Background in site reliability engineering (SRE)