Senior ML & NLP Engineer for GenAI in Finance

RavenPack; Madrid; Presencial; EUR 60.000 - 100.000; Jornada completa

Descripción de la vacante

RavenPack in Madrid is seeking an experienced Machine Learning / NLP Engineer to lead a high-impact infrastructure project. You will design, evaluate, and deploy advanced ML models to enhance our large-scale detection pipeline, working with core engineering and data teams.

The role focuses on modern NLP techniques, low-latency inference, and seamless integration with existing systems while maintaining robust CI/CD for ML deployments.

Formación

• Deep theoretical and practical understanding of core NLP tasks.
• Experience with transformer-based models and encoding methods.
• Proven track record deploying ML models for low-latency, scalable systems.
• Experience designing ML evaluation frameworks and datasets.
• Experience combining probabilistic models with legacy software; A/B testing.
• Strong coding skills, documentation, and CI/CD for ML deployments.

Responsabilidades

• Design, train, and deploy NLP models to complement and scale our existing high-throughput data architecture.
• Architecture Integration: Build robust integration layers that allow new ML predictions to work with our production systems.
• Continuous Improvement Pipelines: Develop automated feedback loops for systems that learn and adapt over time.
• Production Deployment & Optimization: Transition models from research to production with low latency and reliability.
• Quality & Evaluation Frameworks: Establish benchmarks, automated testing and monitoring for ML implementations.

Conocimientos

Advanced NLP

Transformer Models

MLOps

Evaluation Metrics

System Integration

Software Engineering

Descripción del empleo

RavenPack in Madrid is seeking an experienced Machine Learning / NLP Engineer to lead a high-impact infrastructure project. You will design, evaluate, and deploy advanced ML models to enhance our large-scale detection pipeline, working with core engineering and data teams.

The role focuses on modern NLP techniques, low-latency inference, and seamless integration with existing systems while maintaining robust CI/CD for ML deployments.