1 to 25 of 397 Permanent LLM Jobs in Central London

systems into the wider enterprise architecture. Technical Skillset Requirements Core AI & Frameworks: Agentic Frameworks: Expert-level knowledge of agentic frameworks such as LangChain, LangGraph, Google Agent Development Kit (ADK) LLM Expertise: Advanced Prompt Engineering and hands-on experience with model fine-tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and Llama 3. RAG & Vector Databases: Deep More ❯

designing, developing, deploying, and monitoring machine learning and GenAI solutions Experience working with data scientists and/or ML engineers and building auto-scaling ML systems Deep knowledge of LLM architectures and attention mechanisms – experienced with MoE, MHA, SWA, Flash Attention, GPT, LLaMA, DeepSeek, and reasoning-based models Experience in creating and maintaining RAG supported systems including PDF extraction, OCR More ❯

designing, developing, deploying, and monitoring machine learning and GenAI solutions Experience working with data scientists and/or ML engineers and building auto-scaling ML systems Deep knowledge of LLM architectures and attention mechanisms – experienced with MoE, MHA, SWA, Flash Attention, GPT, LLaMA, DeepSeek, and reasoning-based models Experience in creating and maintaining RAG supported systems including PDF extraction, OCR More ❯

domains such as reliability, corrosion, WRFM, and process engineering. Exposure to customer platforms and trading ERP systems. Experience with Azure ML Studio, Foundry, or Databricks Mosaic. Knowledge of NLP, LLM deployment, Agentic AI (MCP, A2A, N8N), and RAG systems. This is a fantastic opportunity to work on high-impact AI projects in a collaborative and forward-thinking environment. If you More ❯

domains such as reliability, corrosion, WRFM, and process engineering. Exposure to customer platforms and trading ERP systems. Experience with Azure ML Studio, Foundry, or Databricks Mosaic. Knowledge of NLP, LLM deployment, Agentic AI (MCP, A2A, N8N), and RAG systems. This is a fantastic opportunity to work on high-impact AI projects in a collaborative and forward-thinking environment. If you More ❯

ready applications that drive automation, optimisation, and intelligence across multiple industries. What You’ll Be Doing: Designing, developing, and deploying machine learning and AI models Designing, developing, and deploying LLM applications (e.g. GPT, LLaMA, Claude) integrated with RAG pipelines Implementing end-to-end workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Building scalable pipelines More ❯

ready applications that drive automation, optimisation, and intelligence across multiple industries. What You’ll Be Doing: Designing, developing, and deploying machine learning and AI models Designing, developing, and deploying LLM applications (e.g. GPT, LLaMA, Claude) integrated with RAG pipelines Implementing end-to-end workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Building scalable pipelines More ❯

across data science, engineering, and business teams to shape and implement transformative AI strategies, from concept through to scalable deployment. What you’ll do: ● Lead design and architecture of LLM-powered and multi-agent AI systems ● Advise senior stakeholders and run AI strategy workshops ● Oversee end-to-end solution delivery across industries such as finance, insurance, and utilities ● Mentor teams More ❯

across data science, engineering, and business teams to shape and implement transformative AI strategies, from concept through to scalable deployment. What you’ll do: ● Lead design and architecture of LLM-powered and multi-agent AI systems ● Advise senior stakeholders and run AI strategy workshops ● Oversee end-to-end solution delivery across industries such as finance, insurance, and utilities ● Mentor teams More ❯

and NLP. Strong programming skills in Python; proficient in libraries such as NumPy, Pandas, scikit-learn, PyTorch, TensorFlow. Proven experience building and scaling GenAI applications in production. Expertise in LLM architectures (OpenAI, LLaMA, Mistral, etc.) and RAG implementation. Familiarity with frameworks like LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, or similar. Experience with CI/CD for AI (GenAIOps), model monitoring, and More ❯

and NLP. Strong programming skills in Python; proficient in libraries such as NumPy, Pandas, scikit-learn, PyTorch, TensorFlow. Proven experience building and scaling GenAI applications in production. Expertise in LLM architectures (OpenAI, LLaMA, Mistral, etc.) and RAG implementation. Familiarity with frameworks like LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, or similar. Experience with CI/CD for AI (GenAIOps), model monitoring, and More ❯

and data science. Experience building, deploying, and scaling AI models in Azure and/or AWS environments. Skilled in using LangChain, LangGraph, vector databases, and prompt engineering. Understanding of LLM optimisation, including context extension and RAG-based frameworks. Proven ability to collaborate cross-functionally and communicate complex AI concepts to non-technical teams. A proactive, creative mindset — comfortable operating with More ❯

and data science. Experience building, deploying, and scaling AI models in Azure and/or AWS environments. Skilled in using LangChain, LangGraph, vector databases, and prompt engineering. Understanding of LLM optimisation, including context extension and RAG-based frameworks. Proven ability to collaborate cross-functionally and communicate complex AI concepts to non-technical teams. A proactive, creative mindset — comfortable operating with More ❯

are looking for: Research engineering: 5+ years of industry experience in complex machine learning projects including data processing and training models in a distributed environment. LLMs: Deep understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers More ❯

are looking for: Research engineering: 5+ years of industry experience in complex machine learning projects including data processing and training models in a distributed environment. LLMs: Deep understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers More ❯

in AI orchestration and agentic reasoning What Were Looking For Proven experience with LangGraph (bonus: contributions to the open-source project) Strong background in Python, LangChain, OpenAI APIs, and LLM architectures Familiarity with vector databases, retrieval-augmented generation (RAG), and prompt engineering Understanding of software design principles, version control (Git), and CI/CD practices Creative problem-solver with a More ❯

AI orchestration and agentic reasoning What We’re Looking For Proven experience with LangGraph (bonus: contributions to the open-source project) Strong background in Python, LangChain, OpenAI APIs, and LLM architectures Familiarity with vector databases, retrieval-augmented generation (RAG), and prompt engineering Understanding of software design principles, version control (Git), and CI/CD practices Creative problem-solver with a More ❯

of API design, data modelling, and microservice architecture. Excellent communication skills, with the ability to translate technical outcomes into business impact. Tech Environment Languages: Python, TypeScript, Java AI/LLM: OpenAI, Anthropic, Retrieval-Augmented Generation (RAG) Infrastructure: AWS (Lambda, ECS, S3), Terraform, Docker Databases: PostgreSQL, MySQL, Redis, vector databases DevOps: GitHub, CI/CD pipelines Why Join Competitive salary and More ❯

of API design, data modelling, and microservice architecture. Excellent communication skills, with the ability to translate technical outcomes into business impact. Tech Environment Languages: Python, TypeScript, Java AI/LLM: OpenAI, Anthropic, Retrieval-Augmented Generation (RAG) Infrastructure: AWS (Lambda, ECS, S3), Terraform, Docker Databases: PostgreSQL, MySQL, Redis, vector databases DevOps: GitHub, CI/CD pipelines Why Join Competitive salary and More ❯

the company. What we are looking for: Research engineering: Industry experience in complex machine learning projects including data processing and training models in a distributed environment. LLMs: Understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Proficiency in Python using PyTorch/TensorFlow/JAX framework. Cloud-native technologies: Experience in developing and deploying More ❯

the company. What we are looking for: Research engineering: Industry experience in complex machine learning projects including data processing and training models in a distributed environment. LLMs: Understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Proficiency in Python using PyTorch/TensorFlow/JAX framework. Cloud-native technologies: Experience in developing and deploying More ❯

looking for: Research engineering: 2-3 years of industry experience in complex machine learning projects including data processing and training models in a distributed environment. LLMs: Strong understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers More ❯

looking for: Research engineering: 2-3 years of industry experience in complex machine learning projects including data processing and training models in a distributed environment. LLMs: Strong understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers More ❯

experimentation insights, and root cause analyses. Build and scale machine learning algorithms and pipelines to production using big data technologies. Develop and deploy retrieval-augmented generation (RAG) systems and LLM-based applications. Design and evaluate A/B tests and communicate results across cross-functional teams. Define, implement, and monitor key performance metrics for AI-driven product features. Stay up More ❯

experimentation insights, and root cause analyses. Build and scale machine learning algorithms and pipelines to production using big data technologies. Develop and deploy retrieval-augmented generation (RAG) systems and LLM-based applications. Design and evaluate A/B tests and communicate results across cross-functional teams. Define, implement, and monitor key performance metrics for AI-driven product features. Stay up More ❯