1 to 25 of 241 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 ❯

Infrastructure & cloud : Familiarity with cloud platforms (Azure preferred; AWS or GCP also valuable), containerization (Docker, Kubernetes), and infrastructure-as-code tools like Terraform. Applied AI development: Experience working with LLM APIs (e.g., OpenAI) and building lightweight AI agents. Familiarity with orchestration tools like Temporal is a plus. Collaboration and impact: Strong problem-solving ability, intellectual curiosity, and a pragmatic approach More ❯

Degree in Statistics, Computer Science, or a related field. - 5+ years in data science with at least 2 years leading teams. - Proven success in production deployment of ML/LLM/NLP/CV models. - Strong understanding of machine learning fundamentals, statistical inference, and model evaluation. - Advanced proficiency in SQL (e.g., PostgreSQL, ELT/ETL) and Python (PyTorch, LightGBM, Scikit 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 ❯

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 ❯

technical debt management Ability to establish coding standards and best practices across teams AI/ML Expertise Strong understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance in production Automation Architecture Deep More ❯

Foundry (full training provided). Familiarity with AI/ML Ops pipelines , real-time analytics, or edge deployments. Big Data stack knowledge (e.g., Hadoop, Spark, Kafka). GenAI/LLM experience (e.g., AWS Bedrock, LangChain). Why this is a great move 🌳 Mission & impact: Work on projects where data-driven decisions have real-world consequences. Growth: Multiple openings from mid More ❯

what is key is that you will be deeply involved with productionising and deploying. Strong Python, SQL and programming skills Hands on experience working within areas such as NLP, LLM, recommendation systems Information Retrieval stacks, GenAI (specifically RAG systems). Familiar with architecture and implementation of 1+ ML frameworks (PyTorch, scikit-learn). Experience working in the full model lifecycle More ❯

years of experience in designing, developing, deploying, and monitoring machine learning and GenAI solutions 2+ years of experience in Application development, deployment, and monitoring on Kubernetes Deep knowledge of LLM architectures and attention mechanisms Education: Bachelor’s/Master’s/PHD degree in any of the following: Engineering, statistics, machine learning, computational linguistics or relevant areas Commodity Trading Expertise More ❯

collaboratively with the team to ensure project deadlines are met. 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. Integrate More ❯

agentic platform. Key Responsibilities Collaborate with client teams to understand domain challenges and success metrics. Translate business requirements into scalable AI solutions using the agentic framework. Prototype and deploy LLM-based agents (prompt structures, orchestration logic, memory, retrieval pipelines). Drive the transition from proof-of-concept to production with reliability and measurable ROI. Act as a trusted technical advisor 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 ❯

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 ❯

native development. Python scripting proficiency at platform engineer level but an understanding of software development lifecycle and production system ownership. Interest or experience in GenAI infrastructure - RAG, vector databases, LLM platforms, or agent frameworks. Strong communication skills and ability to translate technical decisions into business outcomes. This role is NOT an ML Engineering position - you won't be training models More ❯

experience writing scalable, production-ready code. Hands-on experience with PyTorch, Transformer architectures, and Large Language Models (LLMs). Demonstrated success delivering Computer Vision and/or NLP/LLM projects into production. Solid understanding of model deployment, pipelines, and software development fundamentals. Expertise in Deep Learning, including training, evaluation, and optimisation. Strong grounding in mathematics, statistics, and data analysis. 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 ❯

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 ❯

Experience delivering and scaling enterprise AI/ML systems Hands-on expertise with cloud AI platforms (AWS, Azure, etc.) Strong Python engineering skills + FastAPI, CI/CD, MLOps, LLM tooling Leadership across cross-functional engineering teams If you are Interested, apply directly or reach out to jonah.brookes@oliverbernard.com Principal AI Engineer | Up to £130K | Hybrid in London (3 Days More ❯

Experience delivering and scaling enterprise AI/ML systems Hands-on expertise with cloud AI platforms (AWS, Azure, etc.) Strong Python engineering skills + FastAPI, CI/CD, MLOps, LLM tooling Leadership across cross-functional engineering teams If you are Interested, apply directly or reach out to jonah.brookes@oliverbernard.com Lead AI Engineer | £90K-£110K + Bonus | Hybrid in London More ❯

on their own two feet and deliver complex solutions with confidence. You’ll be joining a small, sharp team building AI-driven products — think RAG pipelines , GenAI integrations , and LLM-powered features that actually make an impact. This isn’t a role where you hide behind Jira tickets. You’ll be hands-on , trusted to make big decisions, and involved More ❯

clunky PDFs, fragmented data, and manual analysis with multi-agent AI systems that read, reason, and cite financial insights. Why this role stands out: 🤖 Work directly on multi-agent LLM systems powering deep financial research 🧠 Tackle complex AI problems like knowledge graphs, RAG pipelines, citations, and personalisation 🚀 Join a tight-knit 3-person AI team at a company 8 months More ❯

enterprise-grade AI/ML systems. Expertise with cloud AI stacks (AWS Bedrock, SageMaker, Azure OpenAI, Azure ML). Strong skills in Python, FastAPI, CI/CD, MLOps, and LLM orchestration. Leadership experience across multidisciplinary AI teams. Strategic mindset — able to bridge deep tech with business outcomes. Salary - £110,000 - 130,000 Working policy - 3 days onsite weekly If this More ❯

enterprise-grade AI/ML systems. Expertise with cloud AI stacks (AWS Bedrock, SageMaker, Azure OpenAI, Azure ML). Strong skills in Python, FastAPI, CI/CD, MLOps, and LLM orchestration. Leadership experience across multidisciplinary AI teams. Strategic mindset — able to bridge deep tech with business outcomes. Salary - £110,000 - 130,000 Working policy - 3 days onsite weekly If this More ❯