4 of 4 Machine Learning Jobs in Crawley

Project Work with Hugging Face to build a sentiment analysis classifier using real-world AI techniques. Step 8 – AI Prompt Engineering Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems … Combine prompt engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine ...

expected Excellent oral & written communication skills Experience programming using C++ in HPC environments desirable. Knowledge and experience in the application of data science and machine learning to subsurface imaging challenges a bonus Career Advancement: SLB Eureka Technical Career (SETC) provides proven technical contributors with a career path that ...

HaveWhole platform domain exposure including FIN/HCM/Payroll/Security —e.g., approvals, exceptions, vendor onboarding, expense governance, journals, controls, agentic AI and machine learning, lifecycle.Experience in AI product development. Familiarity with Workday BIRT, Workday Reporting (advanced), Time Tracking.Scripting experience (e.g., JavaScript/TypeScript, Python) for tooling ...

feedback, and development planning. 11. Coach and develop as needed, building leadership capability within the team. 12. Foster a culture of psychological safety, accountability, learning, and inclusion. 13. Manage succession planning and retention of critical skills. 14. Role model and uphold the leadership behaviours outlined in the Behavioural Framework. … requirements. 20. Architect cloud networking (VPN, dedicated InterconnecT, peering) and hybrid data movement pipelines between on-prem and cloud. 21. Define the AI/ML infrastructure strategy: platform capabilities required to support distributed training, inference, and MLOps at scale. 22. Establish FinOps practice for HPC: cloud cost visibility, chargeback/ ...