MLOps Developer with Python

About the Position

We are looking for a hands on MLOps and AI Ops engineer to help build, deploy, and operate ML and GenAI products on a shared Data and AI platform. You will focus on execution across model lifecycle activities, production reliability, and the infrastructure that supports both platform environments and delivered products.

About the Project

Airline Platform is a cross domain Data and AI platform that enables teams to deliver reliable, secure, and scalable data and AI and ML products. It uses Google Cloud Platform, BigQuery, Docker, Kubernetes, Terraform, CI and CD pipelines, and GenAI tooling to support the full product lifecycle.

About the Team

You will work within the Shared Services organization focused on Digital Capabilities and Accelerators. The team collaborates across domains to enable platform capabilities and support the delivery and operation of data and AI products.

Responsibilities

• Build, deploy, and operate ML and GenAI models and services end to end following MLOps practices
• Develop and maintain data and AI products within the platform domains
• Enable and harden the shared Data and AI platform including environment and product infrastructure
• Orchestrate and maintain reliable data and model pipelines ensuring quality and availability for downstream consumers
• Apply Infrastructure as Code with Terraform and CI and CD standards for data and AI workloads
• Instrument observability and monitoring for models and services and act on performance and cost
• Participate in design and code reviews
• Document the processes built

Requirements

• Around 3 years of proven experience as an MLE, ML Ops, AI engineer, or similar role
• Strong Python with experience deploying and serving models in production
• Hands on end to end MLOps including model registry, deployment, and monitoring on generic platforms
• Experience with containerization and orchestration using Docker and Kubernetes
• Experience with Infrastructure as Code using Terraform and CI and CD practices
• Experience with Google Cloud Platform including BigQuery
• Fluency in Spanish and intermediate English for documentation and international collaboration

Nice to Have

• Experience with GenAI engineering including LangChain, agents, Model Context Protocol, and vector databases
• Experience with API development using FastAPI or Flask
• Knowledge of data platform fundamentals including lakehouse and warehouse concepts, streaming, SQL transformations, and pipeline orchestration
• Awareness of data governance, cataloging, security, and IAM
• Experience with observability tooling such as Prometheus, Grafana, or Datadog

Technologies

Python, Google Cloud Platform, BigQuery, Docker, Kubernetes, Terraform, CI/CD, LangChain, Model Context Protocol, vector databases, FastAPI, Flask, Prometheus, Grafana, Datadog