Lead MLOps Engineer

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. Own the ML platform . Design, build, and maintain ML serving and deployment infrastructure across AWS and GCP . Manage multi-environment cloud infrastructure for dev, prelive, and live environments using Terraform and

Posted 10/31/2026full-timeBarcelona • SpainSenior💰 €90,000 - €100,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in managing and optimizing ML platforms, including deployment and infrastructure across AWS and GCP. Proven ability to lead engineering teams and deliver end-to-end ML services with a focus on cost efficiency and performance optimization.

Highest-signal resume keywords

ML Platform OwnershipAWS Cloud InfrastructureGCP Cloud InfrastructureTerraformKubernetes Deployment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

ML EngineeringMLOpsGPU Compute OptimizationDeep Learning Model TrainingTriton ServerDockerEvent-Driven ArchitecturesCost OptimizationModel ConversionBatching Strategies

Soft Skills

Team LeadershipTechnical Development

Tools & Technologies

TerragruntFastAPITorchServeArgoCDCursorCopilotChatGPT

Industry Keywords

HealthcareDental-TechRegulated DomainsPrecision-Critical Domains

Tech Stack

Tools & technologies

AWSCloudDockerGoogle Cloud PlatformKubernetesPyTorchTerraform

About the role

Key responsibilities & impact

Own the ML platform
Design, build, and maintain ML serving and deployment infrastructure across AWS and GCP
Manage multi-environment cloud infrastructure for dev, prelive, and live environments using Terraform and Terragrunt
Deploy and operate ML workloads on Kubernetes, including health checks, secrets management, and observability
Optimize GPU inference through profiling, batching, model conversion, and Triton Server
Improve cost efficiency through right-sizing compute, scale-to-zero GPU strategies, monthly infrastructure spend ownership, and cost-per-inference metrics
Lead the engineering team by defining ownership areas, developing engineers technically, and setting platform direction
Bridge ML and product by working with researchers, product managers, and clinical teams to take models from prototype to production

Requirements

What you’ll need

6+ years of experience in ML engineering, MLOps, or ML platform roles with production responsibility
Strong hands-on experience with AWS and/or GCP cloud infrastructure
Proficiency with Terraform and/or Terragrunt
Experience deploying and operating ML inference services in production, including Triton Server, TorchServe, FastAPI, or equivalent
Experience with Docker, including multi-stage builds, image optimization, and container registries
Strong understanding of GPU compute for ML workloads, instance selection, cost optimization, and inference profiling
Demonstrated ability to deliver end-to-end ML services from infrastructure provisioning to production deployment
Experience with event-driven architectures such as SNS, SQS, webhooks, or equivalent message-passing systems
Track record of quantifiable business impact, including cost reductions, automation rates, or throughput improvements
Ability to lead and develop a small engineering team
Experience training deep learning models, PyTorch, segmentation models, or computer vision is a plus
Familiarity with ONNX/TorchScript conversion, quantization, and batching strategies is a plus
Experience with ArgoCD or other GitOps tooling for Kubernetes is a plus
Background in healthcare, dental-tech, or other regulated/precision-critical domains is a plus
Experience with Terragrunt for multi-account, multi-environment Terraform at scale is a plus
Hands-on use of Cursor, Copilot, or ChatGPT to accelerate infrastructure and ML development is a plus

Benefits

Comp & perks

Competitive salary
Teeth aligner and whitening benefits
Collaborative work environment and positive culture
Opportunities to grow within a fast-paced, innovative company
Real start-up experience with big challenges
Fresh fruits and healthy snacks at the office
International and multicultural team