The Role
Build, iterate, and maintain a production ML platform and pipelines. Support Data Scientists with tooling for deployment and monitoring, define MLOps roadmap and best practices, collaborate with Data Engineering and DevOps, and evaluate/integrate new ML frameworks and real-time inference tools.
Summary Generated by Built In
Barcelona, Full-time, Hybrid
Fledge
Company summary
In this role, you will:
For this job, you must:
What Would Be A Plus 🚀
How to Apply
🌈 Diversity, Equity, Inclusion, and Belonging
ℹ️ Important
Fledge
We are a boutique search firm connecting exceptional Talent with the most innovative companies and products that positively impact our societies. We present a tailor-made solution between fine consulting and flexible Talent search process outsourcing:
- Fractional Talent Acquisition Advisory
- Recruitment operation tooling
- On-demand Talent Search
Our client is a high-growth European tech scale-up on a mission to redefine how the world consumes. They believe in a collaborative economy where smart technology makes sustainable, second-hand trade the global norm.
Operating as a major cross-border marketplace in Spain, Italy, and Portugal, they manage a massive ecosystem with hundreds of millions of products. Their goal is to blend the trust of traditional classifieds with the seamless convenience of a modern marketplace.
- 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.
- 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.
- 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.
If you are excited about this opportunity and believe you are a great fit for the role, please send your resume and a note outlining your experience and what motivates you to join a European tech scale-up.
We are committed to enabling everyone to feel included and valued, and we trust our partners to do the same. We believe both the company and its culture are strongest when composed of diverse experiences and backgrounds.
All qualified applicants will receive consideration for employment without regard to age, color, family, gender identity, marital status, national origin, physical or mental disability, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws.
If you have a medical condition or an individual need for an adjustment to our process, and you believe this may affect your ability to be at your best, please let us know so we can discuss how we can best support you and make any necessary adjustments.
In case of any doubts or questions, please contact - [email protected]
Skills Required
- Proven experience building and owning production-ready ML platforms and pipelines (experimentation to monitoring).
- Deep understanding of AWS components such as SageMaker, Lambda, and S3.
- Container orchestration experience with Kubernetes.
- Strong software engineering background with proficiency in Python, Git, and CI/CD workflows.
- Experience with real-time ML architectures and low-latency ingestion/inference (e.g., Kafka).
- Hands-on experience with vector databases or semantic search infrastructure (indexing and retrieval tuning).
- Familiarity with orchestration/tracking tools (Flyte, MLflow, Feast) and standard ML libraries (Pandas, Scikit-learn, TensorFlow or PyTorch).
- Professional proficiency in English and Spanish.
- Hands-on experience with LLMs, RAG architectures, and libraries like LangChain or LlamaIndex.
- Familiarity with Big Data technologies such as Spark or Beam.
- Experience with Data Engineering tools like Airflow, dbt, or DataHub.
- Experience with other cloud platforms (GCP or Azure) in addition to AWS.
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The Company
What We Do
Fledge is the conscious company accelerator, focused on mission-driven for-profit startups addressing the most important problems of the world: poverty, hunger, unemployment, communities, and the environment.
