76 to 100 of 1,131 LLM Jobs in the UK excluding London

modelling and optimisation. Calculus for optimising algorithms and models. Predictive modelling techniques for regression and classification. Time series analysis for handling time-dependant data. Deep learning and neural networks. LLM Operations Expertise in managing and operationalising large language models. Experience in deploying models on cloud platforms (e.g. AWS, Sage maker, Google AI Platform, IBM Watson) IND_PC3 More ❯

modelling and optimisation. Calculus for optimising algorithms and models. Predictive modelling techniques for regression and classification. Time series analysis for handling time-dependant data. Deep learning and neural networks. LLM Operations Expertise in managing and operationalising large language models. Experience in deploying models on cloud platforms (e.g. AWS, Sage maker, Google AI Platform, IBM Watson) IND_PC3 More ❯

Key Responsibilities Design, build, and deploy Python-based AI and ML applications . Research and develop new Generative AI techniques, including fine-tuning and prompt engineering. Develop and maintain LLM-assisted frameworks and automation pipelines. Write clean, maintainable, and well-documented code for scalable deployment. Build and implement testing and evaluation frameworks for AI models. Collaborate with data scientists, software More ❯

technical discovery or architecture definition in a scaling SaaS or fintech environment. Familiarity with event-driven or streaming architectures (Kafka, Apache Flink, etc.). Practical exposure to AI/LLM orchestration frameworks or fine-tuning workflows. Experience designing developer tools, data platforms, or intelligent systems. Interest in or experience mentoring engineers in applied AI. Degree in Computer Science or related More ❯

is a rare opportunity to blend cutting-edge AI research with hands-on, mission-critical infrastructure ownership. The role demands an engineer who is equally adept at orchestrating sophisticated LLM workflows and managing scalable, secure cloud and edge environments. Why Join? Pioneering Work: Lead the charge on complex multi-agent systems using frameworks like LangGraph, pushing the boundaries of applied More ❯

is a rare opportunity to blend cutting-edge AI research with hands-on, mission-critical infrastructure ownership. The role demands an engineer who is equally adept at orchestrating sophisticated LLM workflows and managing scalable, secure cloud and edge environments. Why Join? Pioneering Work: Lead the charge on complex multi-agent systems using frameworks like LangGraph, pushing the boundaries of applied More ❯

Experience working in Agile teams and collaborating across global R&D locations. Demonstrated ability to mentor junior team members, fostering a culture of continuous learning and innovation. Familiarity with LLM evaluation techniques, output scoring, and validation frameworks. Understanding of key concepts such as prompt engineering, RAG, model orchestration, and hallucination detection. Experience in testing for accuracy, relevance, and consistency of More ❯

ML Engineering and MLOps principles (cloud-based pipelines, CI/CD, monitoring, reproducibility). Experience with Python, SQL & Azure (AWS & GCP is also fine). Exposure to GenAI or LLM tools and frameworks is a strong advantage. Strategic thinker with the confidence to lead technically and shape the roadmap for ML within a growing, collaborative team. Desire to apply technology More ❯

ML Engineering and MLOps principles (cloud-based pipelines, CI/CD, monitoring, reproducibility). Experience with Python, SQL & Azure (AWS & GCP is also fine). Exposure to GenAI or LLM tools and frameworks is a strong advantage. Strategic thinker with the confidence to lead technically and shape the roadmap for ML within a growing, collaborative team. Desire to apply technology More ❯

ML Engineering and MLOps principles (cloud-based pipelines, CI/CD, monitoring, reproducibility). Experience with Python, SQL & Azure (AWS & GCP is also fine). Exposure to GenAI or LLM tools and frameworks is a strong advantage. Strategic thinker with the confidence to lead technically and shape the roadmap for ML within a growing, collaborative team. Desire to apply technology More ❯

ML Engineering and MLOps principles (cloud-based pipelines, CI/CD, monitoring, reproducibility). Experience with Python, SQL & Azure (AWS & GCP is also fine). Exposure to GenAI or LLM tools and frameworks is a strong advantage. Strategic thinker with the confidence to lead technically and shape the roadmap for ML within a growing, collaborative team. Desire to apply technology More ❯

s new, it's exciting and it's an opportunity to work with some incredible talent in this space. The role The role is building AI applications based on LLM and models such as GPT and BERT You'll make use of Python programming, Pyspark, tensorflow, HuggingFace, LangChain, RAG techniques, interfacing with diverse data sets. Cloud data platforms and a More ❯

in a fast-paced startup or product-focused environment Passion for AI, automation, and scalable engineering Nice-to-Haves Experience integrating AI models into real-world products Knowledge of LLM frameworks or retrieval-augmented generation (RAG) systems Exposure to event-driven architectures or real-time data streaming Familiarity with observability tools (Prometheus, Grafana, OpenTelemetry) Why Join You'll be part More ❯

bring experience in both AI development and systems engineering, including: Commercial experience building and deploying multi-agent systems Strong Python development skills for AI/ML applications Understanding of LLM orchestration , memory, and tool usage Familiarity with AWS core services , cloud security, and IaC (Terraform/CloudFormation) Proficiency with containerisation (Docker) and Kubernetes-based environments A passion for experimentation , research More ❯

bring experience in both AI development and systems engineering, including: Commercial experience building and deploying multi-agent systems Strong Python development skills for AI/ML applications Understanding of LLM orchestration, memory, and tool usage Familiarity with AWS core services, cloud security, and IaC (Terraform/CloudFormation) Proficiency with containerisation (Docker) and Kubernetes-based environments A passion for experimentation, research More ❯

exhibits good interpersonal skills. Able to prioritise, multi-task, and deliver at pace with an iterative mindset. Experience with modern data platforms such as Databricks or Snowflake is advantageous; LLM API experience is a bonus. What’s in it for you?: Competitive salary that reflects your skills, experience and potential. Discretionary bonus scheme that recognises your hard work and contributions More ❯

in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms (Dialogflow, Lex, Rasa, etc.). Ability to work collaboratively in fast-paced, agile, and multidisciplinary environments. Excellent communication skills More ❯

in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms (Dialogflow, Lex, Rasa, etc.). Ability to work collaboratively in fast-paced, agile, and multidisciplinary environments. Excellent communication skills More ❯

in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms (Dialogflow, Lex, Rasa, etc.). Ability to work collaboratively in fast-paced, agile, and multidisciplinary environments. Excellent communication skills More ❯

in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms (Dialogflow, Lex, Rasa, etc.). Ability to work collaboratively in fast-paced, agile, and multidisciplinary environments. Excellent communication skills More ❯

is a rare opportunity to blend cutting-edge AI research with hands-on, mission-critical infrastructure ownership. The role demands an engineer who is equally adept at orchestrating sophisticated LLM workflows and managing scalable, secure cloud and edge environments. Pioneering Work: Lead the charge on complex multi-agent systems using frameworks like LangGraph, pushing the boundaries of applied AI in More ❯

RAG + AI/ML Pipelines The Role: + Design, prototype, and deploy Generative AI models (LLMs, Transformers, Diffusion models) for enterprise use cases. + Build and fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). + Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. More ❯

RAG + AI/ML Pipelines The Role: + Design, prototype, and deploy Generative AI models (LLMs, Transformers, Diffusion models) for enterprise use cases. + Build and fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). + Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. More ❯

You’ll Do Define and implement the AI strategy, operating model, and governance structure . Architect and scale AI and ML platforms for enterprise-wide adoption. Lead deployment of LLM, RAG, and Generative AI systems at scale. Partner with business, data, and engineering teams to operationalize AI initiatives. Establish MLOps and LLMOps frameworks for lifecycle automation, compliance, and monitoring. Champion More ❯

You’ll Do Define and implement the AI strategy, operating model, and governance structure . Architect and scale AI and ML platforms for enterprise-wide adoption. Lead deployment of LLM, RAG, and Generative AI systems at scale. Partner with business, data, and engineering teams to operationalize AI initiatives. Establish MLOps and LLMOps frameworks for lifecycle automation, compliance, and monitoring. Champion More ❯