226 to 250 of 574 LLM Jobs in London

finance services business builds, deploys, and adopts AI across the business. Expect hands-on engineering, real problem solving, and plenty of collaboration as you help architect, automate, and operationalise LLM-driven services at scale. What You’ll Be Doing: Building backend services and APIs that give secure, governed access to LLM capabilities Developing Python-based GenAI components including prompt orchestration … CI/CD pipelines using Azure DevOps Working closely with platform leads, architects, and SRE teams to ensure stable, scalable operations Supporting benchmarking, evaluation, and experiment tracking to measure LLM performance and cost Contributing to RAG implementations and vector-driven retrieval patterns Helping shape platform patterns, reusable components, and clear documentation Troubleshooting performance issues across distributed systems and cloud services … What You’ll Bring: 5+ years of backend engineering experience, with strong Python at the core Hands-on exposure to GenAI technologies and Large Language Models Practical understanding of LLM evaluation, prompt handling, and operational complexities A DevOps-first approach with experience in CI/CD, observability, and automation (Azure DevOps preferred) Confidence working in regulated enterprise environments with tight More ❯

finance services business builds, deploys, and adopts AI across the business. Expect hands-on engineering, real problem solving, and plenty of collaboration as you help architect, automate, and operationalise LLM-driven services at scale. What You’ll Be Doing: Building backend services and APIs that give secure, governed access to LLM capabilities Developing Python-based GenAI components including prompt orchestration … CI/CD pipelines using Azure DevOps Working closely with platform leads, architects, and SRE teams to ensure stable, scalable operations Supporting benchmarking, evaluation, and experiment tracking to measure LLM performance and cost Contributing to RAG implementations and vector-driven retrieval patterns Helping shape platform patterns, reusable components, and clear documentation Troubleshooting performance issues across distributed systems and cloud services … What You’ll Bring: 5+ years of backend engineering experience, with strong Python at the core Hands-on exposure to GenAI technologies and Large Language Models Practical understanding of LLM evaluation, prompt handling, and operational complexities A DevOps-first approach with experience in CI/CD, observability, and automation (Azure DevOps preferred) Confidence working in regulated enterprise environments with tight More ❯

finance services business builds, deploys, and adopts AI across the business. Expect hands-on engineering, real problem solving, and plenty of collaboration as you help architect, automate, and operationalise LLM-driven services at scale. What You’ll Be Doing: Building backend services and APIs that give secure, governed access to LLM capabilities Developing Python-based GenAI components, including prompt orchestration … CI/CD pipelines using Azure DevOps Working closely with platform leads, architects, and SRE teams to ensure stable, scalable operations Supporting benchmarking, evaluation, and experiment tracking to measure LLM performance and cost Contributing to RAG implementations and vector-driven retrieval patterns Helping shape platform patterns, reusable components, and clear documentation Troubleshooting performance issues across distributed systems and cloud services … What You’ll Bring: 5+ years of backend engineering experience, with strong Python at the core Hands-on exposure to GenAI technologies and Large Language Models Practical understanding of LLM evaluation, prompt handling, and operational complexities A DevOps-first approach with experience in CI/CD, observability, and automation (Azure DevOps preferred) Confidence working in regulated enterprise environments with tight More ❯

finance services business builds, deploys, and adopts AI across the business. Expect hands-on engineering, real problem solving, and plenty of collaboration as you help architect, automate, and operationalise LLM-driven services at scale. What You’ll Be Doing: Building backend services and APIs that give secure, governed access to LLM capabilities Developing Python-based GenAI components, including prompt orchestration … CI/CD pipelines using Azure DevOps Working closely with platform leads, architects, and SRE teams to ensure stable, scalable operations Supporting benchmarking, evaluation, and experiment tracking to measure LLM performance and cost Contributing to RAG implementations and vector-driven retrieval patterns Helping shape platform patterns, reusable components, and clear documentation Troubleshooting performance issues across distributed systems and cloud services … What You’ll Bring: 5+ years of backend engineering experience, with strong Python at the core Hands-on exposure to GenAI technologies and Large Language Models Practical understanding of LLM evaluation, prompt handling, and operational complexities A DevOps-first approach with experience in CI/CD, observability, and automation (Azure DevOps preferred) Confidence working in regulated enterprise environments with tight More ❯

Machine Learning Engineer (LLM/AI Systems) London/Hybrid | 0–3 Years Experience | Competitive Salary Are you obsessed with AI and large language models? We’re an early-stage startup building real-world products powered by LLMs — from intelligent copilots to adaptive automation tools — and we’re looking for curious minds to help us shape the future of AI. … An environment that values learning, creativity, and personal growth over bureaucracy. Perfect For Graduates or junior engineers with a passion for AI/ML looking to break into applied LLM engineering. Researchers or data scientists eager to move from theory to real-world deployment. Builders who want to join an early-stage company where their work genuinely moves the needle. … Machine Learning Engineer, LLM Engineer, AI Engineer, Artificial Intelligence, Deep Learning, NLP, Natural Language Processing, Large Language Models, Generative AI, GenAI, Neural Networks, PyTorch, TensorFlow, Hugging Face, OpenAI, LangChain, RAG, Retrieval-Augmented Generation, Python, Data Science, AI Research, MLOps, Data Pipelines, Prompt Engineering, Model Fine-Tuning, Cloud Computing, AWS, Azure, Google Cloud, AI Infrastructure, Transformers, Reinforcement Learning, Vector Databases, Pinecone More ❯

challenges. What You'll Bring Full-stack engineering expertise with experience in cloud-native application development (AWS preferred). Hands-on experience in building and deploying Generative AI or LLM-based systems at scale. Proficiency in at least two programming languages such as Java, .NET, TypeScript, or Python, along with experience in modern frontend frameworks like React.js or Stencil. Deep … Strong awareness of OWASP Top 10 and a proactive approach to identifying and mitigating security vulnerabilities during development. Experience designing and deploying Retrieval-Augmented Generation (RAG) pipelines, working with LLM APIs (AWS Bedrock, OpenAI, Azure OpenAI), and using frameworks like LangChain or LangGraph. Strong knowledge of SDLC principles, CI/CD pipelines, and modern engineering practices. Excellent communication and collaboration More ❯

Software Engineering Technical Lead - Series A/B FinTech Scale-Up This is a fantastic opportunity to join a fast-growing, well-funded fintech and be part of our journey as we continue to develop and scale our distributed financial More ❯

Our Mission At Palo Alto Networks® everything starts and ends with our mission: Being the cybersecurity partner of choice, protecting our digital way of life. Our vision is a world where each day is safer and more secure than the More ❯

Senior Generative AI Engineer London (2 days per week on-site) Up to £110,000 + bonus + benefits Permanent, hybrid (2 days in London) Method has partnered with a UK technology consultancy pushing the boundaries of applied generative AI More ❯

Senior Generative AI Engineer London (2 days per week on-site) Up to £110,000 + bonus + benefits Permanent, hybrid (2 days in London) Method has partnered with a UK technology consultancy pushing the boundaries of applied generative AI More ❯

If you need support in completing the application or if you require a different format of this document, please get in touch with at UKI.recruitment@tcs.com or call TCS London Office number 02031552100/+44 204 520 2575 with the More ❯

If you need support in completing the application or if you require a different format of this document, please get in touch with at UKI.recruitment@tcs.com or call TCS London Office number 02031552100/+44 204 520 2575 with the More ❯

from customer KBs, and draft responses with human‐in‐the‐loop controls. Structured extraction: Schema‐driven pipelines over unstructured text (and other modalities) using retrieval, tool‐use, and robust LLM prompting. Hybrid anomaly detection: Blend classical time‐series methods (e.g., decomposition, change‐point, forecasting) with LLM‐aware, contextful detectors for seasonality, spikes, step‐changes, and drift. Novelty discovery: Embedding‐based … clustering and drift, topic surfacing, LLM summarization of emerging themes with deduplication and evidence links. Alerting & scoring: Severity/impact ranking, de‐noising, suppression/cool‐downs, routing, and feedback loops. 25% Architect & scale Own reliability, latency, and cost. Design online/offline eval harnesses, canaries, and SLAs; operate GPUs/accelerators where needed. Stand up and harden RAG pipelines … Postgres, pub/sub/streams). Agents & RAG: Fluency with at least one agent framework ( ADK preferred ). Proven track record shipping AI agents and building RAG pipelines. LLM + DS depth: Prompting/tooling, retrieval design, LLM evals; hands‐on with time‐series analysis (forecasting, change‐point, drift). Cloud & ops: Basic infra ownership on GCP (or AWS More ❯

from customer KBs, and draft responses with human‐in‐the‐loop controls. Structured extraction: Schema‐driven pipelines over unstructured text (and other modalities) using retrieval, tool‐use, and robust LLM prompting. Hybrid anomaly detection: Blend classical time‐series methods (e.g., decomposition, change‐point, forecasting) with LLM‐aware, contextful detectors for seasonality, spikes, step‐changes, and drift. Novelty discovery: Embedding‐based … clustering and drift, topic surfacing, LLM summarization of emerging themes with deduplication and evidence links. Alerting & scoring: Severity/impact ranking, de‐noising, suppression/cool‐downs, routing, and feedback loops. 25% Architect & scale Own reliability, latency, and cost. Design online/offline eval harnesses, canaries, and SLAs; operate GPUs/accelerators where needed. Stand up and harden RAG pipelines … Postgres, pub/sub/streams). Agents & RAG: Fluency with at least one agent framework ( ADK preferred ). Proven track record shipping AI agents and building RAG pipelines. LLM + DS depth: Prompting/tooling, retrieval design, LLM evals; hands‐on with time‐series analysis (forecasting, change‐point, drift). Cloud & ops: Basic infra ownership on GCP (or AWS More ❯

streamline workflows. Collaborate with product and engineering teams to integrate prompting strategies into user-facing features and backend systems. Stay current with the latest advancements in prompt engineering techniques, LLM capabilities, and conversational AI research. Play a key role in building and maintaining a centralized library of best-in-class prompts and interaction patterns to ensure consistency and reusability. Person … ML Engineer, Data Scientist or Python development role with a focus on AI/ML applications. Prompt Engineering: Proven ability to design and iterate on prompts for real-world LLM applications, with a strong portfolio of examples. Python Proficiency: Strong command of Python and its use in interacting with APIs and building applications. Collaborative Development: Experience using collaborative coding environments More ❯

streamline workflows. Collaborate with product and engineering teams to integrate prompting strategies into user-facing features and backend systems. Stay current with the latest advancements in prompt engineering techniques, LLM capabilities, and conversational AI research. Play a key role in building and maintaining a centralized library of best-in-class prompts and interaction patterns to ensure consistency and reusability. Person … ML Engineer, Data Scientist or Python development role with a focus on AI/ML applications. Prompt Engineering: Proven ability to design and iterate on prompts for real-world LLM applications, with a strong portfolio of examples. Python Proficiency: Strong command of Python and its use in interacting with APIs and building applications. Collaborative Development: Experience using collaborative coding environments More ❯

language (Python/Java/Typescript) with experience building APIs and integrating backend systems. Solid Machine Learning fundamentals. Model Context Protocol for managing context and tool interfaces for agents. LLM integration patterns, including prompt orchestration and tool calling. Retrieval-Augmented Generation (RAG) for dynamic context injection. Understanding of user-centric design for AI interfaces and intelligent automation. Experience with AI … frameworks (PyTorch, Tensorflow, Hugging Face etc.). Preferred Skills Preferred knowledge of AWS, particularly AWS Bedrock for LLM deployment and orchestration. Self-motivated with a strong growth mindset. Pay range and compensation package Competitive salary and performance-based bonus. Opportunities for professional growth and career advancement. Contribution pension scheme. A collaborative and innovative work environment. More ❯

language (Python/Java/Typescript) with experience building APIs and integrating backend systems. Solid Machine Learning fundamentals. Model Context Protocol for managing context and tool interfaces for agents. LLM integration patterns, including prompt orchestration and tool calling. Retrieval-Augmented Generation (RAG) for dynamic context injection. Understanding of user-centric design for AI interfaces and intelligent automation. Experience with AI … frameworks (PyTorch, Tensorflow, Hugging Face etc.). Preferred Skills Preferred knowledge of AWS, particularly AWS Bedrock for LLM deployment and orchestration. Self-motivated with a strong growth mindset. Pay range and compensation package Competitive salary and performance-based bonus. Opportunities for professional growth and career advancement. Contribution pension scheme. A collaborative and innovative work environment. More ❯

Senior Machine Learning Engineer - Behavioural Modeling & Threat Detection - £160,000+ - Fully Remote UK BASED CANDIDATES ONLY My client is looking for an experienced Machine Learning Engineer ready to play a pivotal role in shaping the technical direction of their behavioural More ❯

Senior Machine Learning Engineer - Behavioural Modeling & Threat Detection - £160,000+ - Fully Remote UK BASED CANDIDATES ONLY My client is looking for an experienced Machine Learning Engineer ready to play a pivotal role in shaping the technical direction of their behavioural More ❯

The bonus potential is unparalleled. The challenges genuinely interesting from a data perspective. The Applied AI Engineer will design, develop, and implement AI/ML and large language model (LLM) solutions to support the firms research, trading, and decision-making activities. You will prize taking ownership of the work, devising your own plans, impacting directly on the forecast. Responsibilities : Design … and implement AI/ML/LLM-driven models, tools, workflows, and agents to support investment professionals Research, prototype, productionise, and deploy software applications using Python Engage with domain experts to identify, evaluate, and execute high-value AI use cases Remain up to date with developments in AI/LLM technology and recommend relevant advancements Requirements : 2-5+ years … experience in an AI-focused enterprise/start-up Strong Python skills Strong practical experience in LLM concepts (context engineering, RAG, tool calling, workflows, multi-agent systems, MCP) Hands-on experience with frameworks like LangChain, Llamaindex, etc., vector stores, LLM APIs (e.g. OpenAI API, Anthropic API), and building MCP servers/clients Financial services/hedge fund experience is desirable More ❯

Senior Machine Learning Engineer page is loaded Senior Machine Learning Engineer Apply locations London, UK time type Full time posted on Posted 5 Days Ago job requisition id R15074 Job Title Senior Machine Learning Engineer Job Description Here at UnderwriteMe More ❯

modern fintech platform. What You’ll Be Doing: Designing, training, and deploying machine learning and deep learning models for financial analytics, credit/risk scoring, and portfolio insights Building LLM-powered tools (e.g. using GPT-class models) that assist with data extraction, document understanding, investor reporting, and internal decision-support Implementing end-to-end AI pipelines: data ingestion, cleaning, feature … and features Collaborating with product and domain experts to translate fintech and capital-markets workflows into well-scoped AI problems and measurable targets Contributing to internal R&D on LLM evaluation, retrieval-augmented generation (RAG), and methods for improving reliability and explainability of models in financial contexts What We’re Looking For: A recently completed AI-focused Master’s degree … e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Experience training, tuning, and evaluating models using real datasets (not just toy examples), including careful validation and error analysis Familiarity with modern LLM tooling and workflows (e.g. using APIs, building simple RAG or prompt-based systems) is highly advantageous Comfortable working in Linux-based development environments, using Git, testing, and basic CI practices More ❯

modern fintech platform. What You’ll Be Doing: Designing, training, and deploying machine learning and deep learning models for financial analytics, credit/risk scoring, and portfolio insights Building LLM-powered tools (e.g. using GPT-class models) that assist with data extraction, document understanding, investor reporting, and internal decision-support Implementing end-to-end AI pipelines: data ingestion, cleaning, feature … and features Collaborating with product and domain experts to translate fintech and capital-markets workflows into well-scoped AI problems and measurable targets Contributing to internal R&D on LLM evaluation, retrieval-augmented generation (RAG), and methods for improving reliability and explainability of models in financial contexts What We’re Looking For: A recently completed AI-focused Master’s degree … e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Experience training, tuning, and evaluating models using real datasets (not just toy examples), including careful validation and error analysis Familiarity with modern LLM tooling and workflows (e.g. using APIs, building simple RAG or prompt-based systems) is highly advantageous Comfortable working in Linux-based development environments, using Git, testing, and basic CI practices More ❯

and engineering teams to bring these ideas to life, helping to shape both the technical direction and the AI strategy. Expect to spend your time on: Building and productionising LLM-based features and GenAI workflows. Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud … have: Around 7 years' experience as a Software Engineer or Machine Learning Engineer. A strong grounding in Python, with hands-on experience building and shipping AI/ML or LLM-driven products. Experience with cloud ML platforms (AWS SageMaker, Bedrock, Vertex AI, etc.). Familiarity with LangChain or similar orchestration frameworks. A solid understanding of MLOps and how to take More ❯