<div> <h3>About us</h3>At RavenPack, we are redefining how structured data powers intelligent agents, automation, and real-time analytics. For over 20 years, we’ve been a leader in big data analytics for financial services—enabling hedge funds, banks, and asset managers to turn raw information into competitive advantage.Now, we’re expanding our vision with <a>Bigdata.com</a>, a next-generation platform where structured datasets are seamlessly integrated, enriched, and made actionable by cutting-edge AI/ML systems. From autonomous metadata agents to automated knowledge graphs, our mission is to transform data onboarding into a strategic growth engine.Join a Company that is Powering the Future of Finance with AIRavenPack has been recognized as the Best Alternative Data Provider by WatersTechnology and has been included in this year’s Top 100 Next Unicorns by Viva Technology. <h3>We’re looking for</h3>We are seeking a highly skilled AI/ML Engineer to join the Structured Data Squad within our Data Division. This role is central to developing the ML-powered ingestion, enrichment, and automation capabilities outlined in the squad’s mandate. You will work on initiatives that range from training and fine-tuning models like BERT, to building prompt/evaluation frameworks for AI agents, to designing systems that automate entity mapping and knowledge graph construction.This is an ideal opportunity for someone who thrives at the intersection of applied machine learning, software engineering, and innovative data product development. You will collaborate closely with Data Scientists, Backend Engineers, and Product Managers to bring automation, scale, and intelligence to structured data workflows.<h3>Responsibilities</h3><ul><li>Machine Learning Development: Train, fine-tune, and deploy ML models (including BERT variants) for entity extraction, classification, and enrichment tasks in structured datasets.</li></ul><ul><li>Prompt &amp; Evaluation Frameworks: Design, implement, and maintain AI prompt/evaluation systems for structured and semi-structured data enrichment, ensuring measurable quality and continuous improvement.</li></ul><ul><li>Agentic Systems: Build and optimize AI agents (augmenters, validators, approvers) that automate data cleaning, metadata enrichment, and validation in alignment with the Structured Data Squad mandate.</li></ul><ul><li>Knowledge Graph Automation: Develop automated entity mapping processes, linking datasets to RavenPack’s knowledge graph with high precision and scalability.</li></ul><ul><li>Tooling &amp; Infrastructure: Leverage MCP tools, Python, and modern ML libraries to deliver robust, maintainable systems integrated into production pipelines.</li></ul><ul><li>Collaboration &amp; Integration: Work closely with Backend Engineers to integrate ML pipelines into ingestion and API delivery systems.</li></ul><ul><li>Innovation &amp; Research: Stay ahead of the curve on advances in ML, LLMs, and agentic workflows, proactively identifying opportunities to improve performance, reduce cost, and expand capability.</li></ul><ul><li>Documentation &amp; Best Practices: Produce clear technical documentation, reusable templates, and training materials for AI/ML workflows.</li></ul><div> </div><h3>Requirements:</h3><ul><li>Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related technical field.</li></ul><ul><li>3+ years of hands-on experience in applied machine learning or AI engineering.</li></ul><ul><li>Strong Python development skills.</li></ul><ul><li>Experience training and fine-tuning transformer-based models, especially BERT and its variants.</li></ul><ul><li>Proficiency with AI prompt/evaluation frameworks.</li></ul><ul><li>Experience building and maintaining agentic systems in production environments.</li></ul><ul><li>Familiarity with MCP tools and LLM-based automation workflows.</li></ul><ul><li>Understanding of entity resolution, metadata enrichment, and automated knowledge graph construction.</li></ul><ul><li>Strong problem-solving skills and ability to work with complex, multi-source datasets.</li></ul><ul><li>Excellent communication skills in English, both verbal and written.</li></ul> <h3>Desirable</h3><ul><li>Experience with statistical classification of structured/unstructured data. </li><li>Knowledge of big data platforms (e.g., Snowflake, Spark) and cloud services (AWS preferred). </li><li>Experience with containerization (Docker) and CI/CD pipelines. </li><li>Exposure to Retrieval-Augmented Generation (RAG) systems. </li><li>Familiarity with financial datasets and domain-specific ontologies.</li></ul> <h3>Whats in it for You?</h3><ul><li>International Culture: With its headquarters in Marbella, Spain, and presence in New York and London, RavenPack takes pride in being a truly diverse global organization.</li></ul><ul><li>Competitive Salary: In RavenPack, we believe that your time and experience needs to be fairly rewarded.</li></ul><ul><li>Continuous learning: We provide the support needed to grow within the team. </li><li>Innovation: Innovation is the key to our success, so we encourage you to speak up and tell us about your vision.</li></ul><ul><li>Hybrid work arrangement: 3 days office and 2 remote per week.</li></ul><ul><li>Shuttle bus: From Malaga, Fuengirola, La Riviera, and Estepona is available for free from the company.</li></ul><ul><li>Diversity is in our DNA! You will work in an international environment (over 29 nationalities and 24 languages spoken!)</li></ul> </div>