MLOps Engineer

Admiral Europe Tech | Admiral Group; Sevilla; On-site; EUR 60,000 - 90,000; Full time

Job summary

Admiral Europe Tech is building and operating the self-service AI/ML platform of the European Data Platform on AWS. You will enable users to deploy models, functions and GenAI agents through CI/CD pipelines in a secure, multi-account environment.

You will work with security, networking and platform teams, align with the target architecture, and help bring ML/GenAI solutions to production across teams and countries. English proficiency is essential.

Qualifications

• Experience building self-service DevOps tools and pipelines.
• AWS multi-account setup: IAM, VPC, KMS, Secrets Manager.
• Infrastructure as code: CDK or CloudFormation; Terraform valued.
• CI/CD: GitLab CI or equivalent; environment-based deployments.
• Python with testing, packaging and Docker.
• English proficiency for collaboration with teams in other countries.
• Experience with AI coding assistants such as Claude Code or GitHub Copilot.

Responsibilities

• Work with security, networking and platform teams to guarantee a secure and stable environment.
• Collaborate with Data Architect to align development with target architecture.
• Partner with MLOps developers and platform users to bring ML and GenAI to production.

Skills

DevOps platform

AWS multi-account

CI/CD pipelines

Python

Docker

Git/Gitflow

English proficiency

GenAI tools

Tools

AWS CDK

CloudFormation

Terraform

GitLab CI

Git

Docker

Kubernetes

Job description

Role Description

Build and operate the self-service AI/ML platform of the European Data Platform on AWS, so that platform users can deploy models, functions and GenAI agents by themselves, securely and through standard CI/CD pipelines.

Role Description

Build and operate the self-service AI/ML platform of the European Data Platform on AWS, so that platform users can deploy models, functions and GenAI agents by themselves, securely and through standard CI/CD pipelines.

Key Objectives

• Build and maintain the platform as a product: templates and CI/CD pipelines that let users release without tickets.
• Define all infrastructure as code and deploy it through pipelines across accounts and environments.
• Apply security by design: least-privilege access, secrets management and policy as code.
• Operationalize ML models and enable GenAI use cases (agents, tools, knowledge bases) on the platform.
• Monitor the platform, control costs and keep the documentation up to date for users.

Main Responsibilities

• Work with the security, networking and platform teams to guarantee a secure and stable environment.
• Work with the Data Architect to keep development aligned with the target architecture.
• Work with other MLOps developers and platform users to take ML and GenAI solutions to production.

Requirements:

This is you!

• You are enthusiastic about working in a European team on a common Data Platform.
• You think about the people who use what you build and use AI tools in your daily work.
• You are organized and get things done!

Must have:

• Platform / DevOps engineering experience: building tools, templates or pipelines used by other teams (self-service, golden paths).
• AWS in a multi-account setup: IAM (roles, resource policies, cross-account access), VPC networking, KMS, Secrets Manager, S3, Lambda, API Gateway, Step Functions, ECR, ECS/Fargate and CloudWatch.
• Infrastructure as code: AWS CDK (Python preferred) or CloudFormation. Terraform experience is also valued.
• CI/CD: GitLab CI (or equivalent), Git and Gitflow, with deployment by environment.
• Python with software engineering practices (tests, packaging, debugging) and Docker.
• Good level of English to work with teams from other countries.
• Experience working with AI coding assistants such as Claude Code or GitHub Copilot.

Nice to have:

• MLOps: SageMaker (Pipelines, Model Registry, endpoints, Feature Store), MLflow.
• GenAI on AWS: Bedrock, AgentCore, MCP, RAG / Knowledge Bases, agent evaluation.
• Policy as code and service-to-service authentication and authorization (OAuth, Cognito).
• Observability and troubleshooting of distributed systems (network, DNS, TLS, IAM).
• Kubernetes / EKS.
• Data tools: Athena, Glue, Spark / PySpark, Collibra.