Flexible Mlops Engineer: Build Scalable Ml Pipelines

Bloodflow; Viseu; Híbrido; EUR 60 000 - 110 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Competitive compensation and benefits

Equity participation

Professional development and training

Impact on patient care

Resumo da oferta

BloodFlow is building an AI platform that interprets blood test results in a clinical context to aid doctors in decision-making. We’re deploying ML models with robust ML infrastructure, Kubernetes, and CI/CD pipelines to production.

You’ll work on scalable cloud and on-prem deployments, with a focus on reliability, security, and regulatory compliance. The role involves designing and maintaining production ML systems, monitoring performance and drift, and ensuring GDPR/HIPAA compliance in medical

Qualificações

• 3+ years experience in DevOps, MLOps, or production ML systems.
• Familiarity with Docker and cloud platforms.
• Experience with monitoring tools and infrastructure as code.
• Understanding of GDPR, HIPAA, or medical device compliance.

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 cloud and on-prem deployments.
• Manage model versioning, rollbacks, and A/B testing infrastructure.
• Implement monitoring for model performance and drift detection.
• Build dashboards and alerting for production AI systems.
• Track latency, accuracy, throughput, and resource utilization.
• Ensure healthcare audit/tracing compliance and data privacy.
• Implement security best practices for AI deployment.
• Support regulatory requirements for medical device certification.

Conhecimentos

DevOps/MLOps experience

Python

Docker

Cloud platforms

SRE knowledge

GDPR/HIPAA awareness

Ferramentas

MLflow

Kubeflow

Vector databases

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.

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
• Docker, and cloud platforms Experience with
• 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
• 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)

What We Offer

Competitive compensation and benefits package Build critical infrastructure for healthcare AI Work with modern MLOps tools and cloud technologies Impact on patient care through reliable AI systems Competitive salary with equity participation Professional development and training opportunities

Location / Details

Flexible Mlops Engineer: Build Scalable Ml Pipelines Piedade, Portuguese Republic, PT