Lead MLOps Engineer

About this role

🌟 Join Impress – Europe’s Leading Health-Tech Innovator! We believe everyone deserves a smile they’ll love 😁. As the largest Ortho clinic chain in Europe, we combine cutting-edge tech with expert care, revolutionizing invisible orthodontics since 2019. With 150+ clinics across 10 countries 🌍 and rapid growth, we’re improving lives worldwide. We are looking for an Lead MLOps Engineer to join our Onsite Team in Barcelona.

📢 Why we’re cool: Work with an international and multicultural team Competitive salary Teeth aligner and whitening benefits Collaborative work environment and positive culture Opportunities to grow within a fast-paced, innovative company and real start-up experience with big challenges Fresh fruits and healthy snacks at the office 🔥 What You'll Do: Own the ML Platform : Design, build and maintain ML serving and deployment infrastructure across AWS and GCP.

Manage Cloud Infrastructure: Multi-environment setup (dev, prelive, live) using Terraform and Terragrunt. Run ML on Kubernetes: Deployments, health checks, secrets management and observability. Optimize GPU Inference: Profiling, batching, model conversion (ONNX/TorchScript), Triton Server. Drive Cost Efficiency : Right-size compute, build scale-to-zero GPU strategies, own monthly infra spend and cost-per-inference metrics.

Lead the Team: Define ownership areas, grow engineers technically, set platform direction. Bridge ML & Product: Work with researchers, PMs and clinical teams to take models from prototype to production. What We Are Looking For: 6+ years of experience in ML engineering, MLOps, or ML platform roles with production responsibility; Strong hands-on experience with cloud infrastructure — AWS (Lambda, Batch, Step Functions, S3, EC2, ECS/EKS, DynamoDB, RDS) and/or GCP (Cloud Run, Artifact Registry); Proficiency with Infrastructure as Code — Terraform and/or Terragrunt for managing multi-environment cloud deployments; Experience deploying and operating ML inference services in production (Triton Server, TorchServe, FastAPI, or equivalent); Experience with Docker — multi-stage builds, image optimization, container registries; Strong understanding of GPU compute for ML workloads — instance selection, cost optimization, inference profiling; Demonstrated ability to deliver end-to-end ML services from infrastructure provisioning to production deployment; Experience with event-driven architectures — SNS, SQS, webhooks, or equivalent message-passing systems; Track record of quantifiable business impact — cost reductions, automation rates, throughput improvements; Ability to lead and develop a small engineering team.