Senior Machine Learning / NLP Engineer (Contractor)

About the role

RavenPack, a big data analytics provider for financial services with 23 years of experience, is launching a new suite of GenAI and SaaS services for financial professionals. It is looking for an experienced Machine Learning / NLP Engineer to lead a high-impact infrastructure project: designing, evaluating and deploying advanced ML models alongside its large-scale, high-precision detection pipeline. The goal is a hybrid architecture that adds modern ML approaches to the existing infrastructure, tackling low-latency inference, massive-scale entity resolution and continuous learning. European legal working status is required.

Key responsibilities

• Design, train and deploy state-of-the-art NLP models to complement and scale the existing high-throughput data architecture.
• Build integration layers so new ML predictions work seamlessly with established production systems.
• Develop automated feedback loops and learning mechanisms that let systems adapt and improve with minimal manual intervention.
• Move models from research and evaluation into production, optimising them for low-latency inference and high reliability.
• Set up evaluation benchmarks, automated testing harnesses and monitoring dashboards to guarantee accuracy and stability.

Required profile

• Proven experience training, fine-tuning and deploying transformer-based models for complex text tasks.
• Strong track record of optimising models for low-latency environments (batch inference, model distillation, hardware acceleration) and deploying them at scale.
• Experience combining probabilistic ML models with deterministic or legacy software, including confidence scoring and A/B testing rollouts.
• Ability to write clean, scalable code, maintain CI/CD pipelines for ML models and produce technical documentation.

Required skills

• NLP and deep learning: entity extraction, text classification, contextual understanding.
• Transformer architectures and encoding methods.
• MLOps and production engineering.
• ML evaluation frameworks and benchmark design.