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 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)

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

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