• Work on Sophea AI across LLM pre-training, training from scratch, fine-tuning, evaluation, and continuous model improvement • Build production-grade ML pipelines for inference, serving, deployment, monitoring, and model lifecycle management • Optimize model performance in production, including latency, throughput, cost efficiency, quantization, and GPU workload usage • Work with datasets, experiments, benchmarks, and evaluation methods to improve language model quality and domain-specific performance

• Develop, train, and fine-tune machine learning models for multimodal sensor data (e.g., vision, LiDAR). • Implement scalable data preprocessing and augmentation pipelines. • Help develop embedding-based search tools and 'active learning' workflows. • Help build and maintain dashboards to monitor model health, data drift, and system performance. • Follow software engineering standards for ML code and participate in code reviews. • Work closely with senior engineers and machine learning engineers to translate model prototypes into maintainable engineering solutions.

• You'll own the productionisation of our machine learning work end to end: real-time serving against SLAs, a standardised CI/CD path to production, GenAI and RAG system deployment, and the monitoring infrastructure that catches drift and quality degradation before business KPIs move. • You'll work on a GCP-native stack — Vertex AI, BigQuery, dbt, Airflow, and Terraform — on problems that matter from day one, with the autonomy to shape how the function scales and who joins it next. • You'll collaborate closely with Data Scientists, Platform, and Engineering, acting as both a technical owner and an advocate for Data Science needs across the organisation.

Role Description As a Senior ML / MLOps Engineer, you'll be a foundational hire in our new Data Science & Applied AI function — working in close partnership with our Data Scientists to take intelligent systems from prototype to production, and keep them performing once they're live. - Own the productionisation of our machine learning work end to end: - Real-time serving against SLAs - A standardised CI/CD path to production - GenAI and RAG system deployment - Monitoring infrastructure that catches drift and quality degradation before business KPIs move - Work on a GCP-native stack — Vertex AI, BigQuery, dbt, Airflow, and Terraform - Collaborate closely with Data Scientists, Platform, and Engineering - Act as both a technical owner and an advocate for Data Science needs across the organisation Qualifications - A track record of putting models into production alongside Data Scientists — classic ML, LLM/GenAI, or both - Deep understanding of the ML lifecycle from prototype to production, including real-time serving, feature stores, autoscaling, and blue/green deployments - Strong Python and SQL skills - Hands-on GCP experience, especially Vertex AI, Kubeflow pipelines, and BigQuery — or strong fluency on an equivalent cloud with a clear ability to get to Vertex fast - Solid CI/CD and production-monitoring experience, including model registries, versioning, drift detection, and automated retraining - Conversational-level English language skills Requirements - Experience with LLM evaluation, observability tooling, or RAG systems in production - Experience defining MLOps standards across multiple teams - Exposure to cost and latency management for GenAI systems Benefits - This is a fully remote position across the UK, Spain, and Poland. - Competitive salary plus bonus. - Private health cover, life insurance, pension, and enhanced parental leave — with specifics tailored to your location. - A generous holiday allowance plus public holidays. - Access to a global learning and development programme, wellbeing support, and discounts across partner platforms.