MLOps Engineer

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

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Join our growing team of talented individuals

Everything you need to know about this role at Blood
Flow

About Blood
Flow

At Blood
Flow, 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 Dev
  Ops, 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 (Py
  Torch, Tensor
  Flow)
• 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)

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