Senior Machine Learning & AI Infrastructure Engineer (AI Engineering)

TL;DR

Senior Machine Learning & AI Infrastructure Engineer (AI Engineering): Building and operating hirify.global's ML and AI platform, with a strong focus on Feature Store and MLOps workflows. Focus on AI-assisted automation for MLOps and Feature Store, integrating agents and AI services to automate key parts of the workflows.

Location: Hybrid (Madrid / Barcelona)

Company

hirify.global enables global companies to collect payments in 40 emerging markets, focusing on increasing conversion rates and simplifying payment expansion.

What you will do

• Build and evolve the Feature Store, implementing and maintaining online and offline feature pipelines.
• Implement and improve training and evaluation pipelines, focusing on reproducible workflows and model registry integration.
• Work on online and batch inference paths, including model packaging, deployment, and rollout strategies.
• Integrate and extend agents and AI services to automate key parts of the Feature Store and MLOps workflows.
• Implement changes that respect platform standards around access control, secrets management, and PII handling.
• Collaborate with MLOps Technical Referent, Data Science squads, the AI Team, and product squads to align on architecture and unblock use cases.

Requirements

• Solid experience as a Senior Engineer working on MLOps, data platforms, or large‑scale backend / distributed systems.
• Hands‑on experience with big data / streaming technologies (e.g. Spark, Flink, Kafka, Kinesis, or similar).
• Proven track record building production‑grade ML pipelines, including experiment tracking and CI/CD for models and data pipelines.
• Familiarity with cloud‑based ML platforms and containerized deployments (e.g. Databricks, SageMaker, Vertex AI, or equivalent).
• Strong understanding of observability, including metrics, logs, traces, and data/model drift checks.
• Ability to write clean, maintainable code and collaborate through reviews, design docs, and pairing sessions.

Nice to have

• Experience working with or around Feature Stores (Feast, Databricks Feature Store, custom implementations, etc.).
• Exposure to LLMs, agents and AI assistants, especially applied to developer productivity or incident analysis.
• Experience in Fintech, risk, fraud or anomaly detection environments.

Culture & Benefits

• Flexibility: flexible schedules focused on impact and performance.
• Fintech industry: dynamic environment with opportunities to build and boost creativity.
• Learning & development: access to a Premium Coursera subscription.
• Language classes: free English, Spanish, or Portuguese classes.
• Social budget: monthly budget for team activities.