MLOps Engineer (AI)

TL;DR

MLOps Engineer (AI): Building and operating a scalable data platform for ingesting real-time events and transitioning ML models from experimentation to production with an accent on MLOps best practices and ML infrastructure for model serving. Focus on solving significant data validation, data modelling, and scaling challenges, and shaping LLMOps architecture.

Location: Fully Remote, must be based in Spain

Company

hirify.global is a profitable, PE-backed online services marketplace operating in nine countries, undergoing a transformation from a lead generation model to a full marketplace platform.

What you will do

• Build and operate a scalable data platform ingesting real-time events with high throughput.
• Collaborate with Data Scientists to transition ML models from experimentation to production.
• Build and maintain ML infrastructure for model serving and track model performance and lifecycle.
• Collaborate with analysts, engineers, and product managers to produce new event tracking functionality.
• Implement automations, high data quality controls, ensure data integrity, and create necessary monitoring alerts.

Requirements

• Experience deploying and maintaining Python services in AWS (Kinesis, Lambdas, Glue, Firehose, Athena).
• Experience with MLOps frameworks and experiment tracking tools (AWS Sagemaker, MLFlow, Databricks, W&B).
• Basic knowledge of ML Inference REST APIs (FastAPI).
• Great skills improving and operating Schema Registries and Data Catalogs (Glue, Databricks).
• Relevant CI/CD experience (GitHub Actions, Gitlab Pipelines) automating tests and updates.
• Solid experience with SQL and data warehousing/data lake environments (Databricks, BigQuery, Redshift, S3).

Nice to have

• Hands-on experience in a cloud data platform for event streaming (Kinesis, Pub-Sub, Kafka).
• Knowledge and practical experience with data modelling (proto or AVRO schemas) and managing schema evolution.
• Production experience with containerization (Docker) and orchestration (Kubernetes, AWS ECS/Fargate).
• Familiarity with IaC (Terraform, Crossplane) and LLMOps (LangGraph).
• Experience with large scale data processing with PySpark and Flink or Datamart creation with DBT or Airflow job orchestration.

Culture & Benefits

• Agile and innovative scale-up environment with financial stability and resources for growth.
• Genuine ownership, responsibility, and opportunity to shape commercial strategy.
• Opportunity to contribute to a pivotal transformation phase of a global marketplace platform.
• Global team of over 220 people.
• Culture defined by excitement, ambition, and commitment to raising the bar.
• Dedicated to diversity, equity, inclusion, and belonging.

Hiring process

• Screening Call with Talent Partner.
• Hiring Manager Stage.
• Technical Interview.
• Values interview.