Barcelona, Full-time, Hybrid 

Fledge

• Fractional Talent Acquisition Advisory
• Recruitment operation tooling
•  On-demand Talent Search

Company summary

In this role, you will:

• Iterate and maintain the ML Platform, identifying opportunities to improve speed, reliability, and maintainability. You will define the long-term vision and roadmap for MLOps.
• Work hand-in-hand with Data Scientists to support their efforts, ensuring they have the tooling to develop, deploy, and monitor scalable models efficiently.
• Define and promote engineering best practices (coding standards, testing, CI/CD) within the ML domain.
• Partner with Data Engineering and DevOps to align ML development with company-wide infrastructure and data governance standards.
• Investigate and integrate new frameworks and tools (e.g., for LLMs or real-time inference) to keep our stack modern and effective.

For this job, you must:

• Proven experience building and owning production-ready ML platforms and pipelines. You understand the full lifecycle from experimentation to monitoring.
• Deep understanding of AWS components (SageMaker, Lambda, S3) and container orchestration with Kubernetes.
• Strong software engineering background with proficiency in Python, Git, and CI/CD workflows. You write robust, testable code.
• Experience with real-time ML architectures, leveraging tools like Kafka for low-latency ingestion and inference.
• Hands-on experience with vector databases or semantic search infrastructure (e.g., OpenSearch, Vertex AI), including indexing and retrieval tuning.
• Familiarity with the broader ML toolkit, such as orchestration/tracking tools (Flyte, MLFlow, Feast) and standard libraries (Pandas, Scikit-learn, TensorFlow/PyTorch).
• Professional proficiency in English and Spanish, with the ability to explain complex technical concepts to diverse stakeholders.

What Would Be A Plus 🚀

• Hands-on experience working with LLMs, RAG architectures, and libraries like LangChain or LlamaIndex.
• Familiarity with Big Data technologies like Spark or Beam.
• Experience with Data Engineering tools such as Airflow, dbt, or Datahub.
• Experience with other cloud platforms like GCP or Azure in addition to AWS.

How to Apply

🌈 Diversity, Equity, Inclusion, and Belonging

ℹ️ Important