3 of 3 Machine Learning Jobs in Chester

design. All underpinned by technology created with purpose. We’re looking for GenAI/Agentic Engineers who want to shape the future of Machine Learning, Generative AI and Agentic AI. You will be tasked with building and deploying intelligent systems that combine cutting-edge models, agentic frameworks … pitch contribution, and client hosting at events. Internal contribution – Campaign development, internal think-tanks, whitepapers, practice development (operations, recruitment, team events & activities), offering development. Learning & development – Training to support your career development and the skills demand within the company, certifications etc. YOUR PROFILE We’d love to meet someone ...

client pitch contribution, client hosting at events. Internal contribution – Campaign development, internal think-tanks, whitepapers, practice development (operations, recruitment, team events & activities), offering development. Learning & Development – Training to support your career development and the skills demanded within the company, accreditations, certifications etc. YOUR PROFILE We are looking for individuals … evaluation and Responsible AI AI orchestration frameworks e.g. LangChain, Semantic Kernel, Model Context Protocol (MCP) Data engineering, pipelines, modelling and governance Advanced analytics, machine learning and statistical modelling Data visualisation and insight solutions Candidates should understand the importance of trusted, accessible and AI-ready data, and be curious ...

connecting PLM, MBSE, ERP, MES, engineering data and the wider enterprise architecture. Define and lead AI-enabled engineering strategies, including generative AI, engineering copilots, machine learning and intelligent automation, integrating them with digital thread, digital twin, simulation and software-defined product approaches to deliver measurable value. Shape responsible … scaled adoption, and business outcomes. • Experience identifying, assessing and scaling high-value AI use cases across the engineering lifecycle, including generative AI, engineering copilots, machine learning and intelligent automation. • Strong understanding of AI and data foundations, including data quality, knowledge architectures, platform integration, responsible AI governance, privacy, security ...