MLOps Engineer (AdTech)

Our customer is an adtech product company with a truly global presence, specializing in mobile app monetization. They provide a platform that helps developers grow their businesses through intelligent mediation and cutting-edge data tools. The team is composed of international professionals dedicated to building history-defining solutions for the mobile industry.

We are looking for an MLOps Engineer who will play a key role in building, maintaining, and scaling an ML platform, powering real-time predictions at a massive scale. You will work on training pipelines, optimize GPU workloads, manage model promotion workflows, and ensure reliable online inference, collaborating closely with Data Science, Data Engineering, and Runtime teams.

Their product processes large, complex data streams, so we are looking for someone confident working with high-volume, high-performance systems.

This role is a hybrid position based in the Barcelona office.

What You'll Drive

• Design, operate, and improve ML infrastructure: Databricks workflows, MLflow, Unity Catalog, and CI/CD pipelines via GitHub Actions;

• Support Data Scientists with end-to-end training pipelines, including data processing, feature transformations, GPU-based model training, and distributed setups;

• Ensure reliable, low-latency inference by validating models and monitoring production systems;

• Maintain strong ML observability: online/offline monitoring, feature parity, automated model performance validation;

• Build internal tools and APIs to enable experimentation, validation, and model promotion;

• Collaborate across multiple teams while owning systems and projects independently.

Who You Are

• AdTech Domain experience or a closely related field;

• 3+ years in MLOps, ML Platform Engineering, or backend roles supporting production ML systems;

• Strong Python and software engineering skills; API development (FastAPI, Flask);

• Hands-on experience with PyTorch or equivalent deep learning frameworks; GPU training optimization;

• Distributed computing and data processing experience (Apache Spark or similar);

• Experience with Databricks (Jobs, MLflow, Delta, Unity Catalog) or equivalent cloud ML platforms;

• Solid understanding of ML lifecycle: data ingestion, model packaging, deployment, and monitoring;

• Familiarity with real-time inference systems and downstream integration;

• Data validation experience (e.g., Pydantic);

• Docker, Kubernetes, basic networking knowledge;

• CI/CD pipeline management (GitHub Actions or equivalent);

• Orchestration tools experience (Databricks Jobs, Dagster, Airflow);

• Proactive, autonomous work style with strong English communication skills.

Nice to have:

• Exposure to high-load, low-latency inference environments;

• Distributed/large-scale ML training, multi-GPU setups;

• Databricks Asset Bundles knowledge;

• ONNX or portable model formats;

• Rust programming experience;

• Transformer architectures, sequence models;

• Data engineering skills (CDC, scalable pipelines, structured/unstructured data);

• MinIO or S3-compatible storage experience;

• Networking protocol expertise (TCP, HTTP, gRPC) for low-latency systems;

• Familiarity with Atlassian tools (Jira, Confluence, Compass).

What You'll Get

• Career Growth: Professional development opportunities and a clear path for advancement within a fast-growing environment
• Work-Life Balance: Flexible work arrangements and a culture that respects personal time
• Relocation Support: Assistance with relocating to a vibrant hub in Barcelona
• Impactful Projects: The chance to work on high-stakes ML projects that influence the global mobile ecosystem

If you're excited about MLOps and ready to take ownership of a critical ML platform, we'd love to hear from you!