26 to 50 of 122 Prompt Engineering Jobs in London

AI Engineer - Elite Sports Tech - London AI Engineer needed to join a highly skilled Engineering & AI team in a rapidly growing sports tech company. Role and Responsibilities: Design, build, and deploy Large Language Model (LLM)-driven applications into production Develop retrieval-augmented generation (RAG) systems for real-time insights from performance and wearable data Optimise and fine-tune LLMs … and scalable backend components in Python Collaborate cross-functionally with engineers, data scientists, designers, and product managers to ship AI-first features Requirements: 2+ years of hands-on AI Engineering experience, with strong software engineering fundamentals Proven experience working with LLMs in production (fine-tuning, prompt engineering, or API integrations) Proficiency in Python Experience designing and More ❯

AI Engineer - Elite Sports Tech - London AI Engineer needed to join a highly skilled Engineering & AI team in a rapidly growing sports tech company. Role and Responsibilities: Design, build, and deploy Large Language Model (LLM)-driven applications into production Develop retrieval-augmented generation (RAG) systems for real-time insights from performance and wearable data Optimise and fine-tune LLMs … and scalable backend components in Python Collaborate cross-functionally with engineers, data scientists, designers, and product managers to ship AI-first features Requirements: 2+ years of hands-on AI Engineering experience, with strong software engineering fundamentals Proven experience working with LLMs in production (fine-tuning, prompt engineering, or API integrations) Proficiency in Python Experience designing and More ❯

Diffusion models) for real-world enterprise use cases. Build and fine-tune LLM-based applications such as: - Chatbots - Document Q&A systems - Report generators - Code assistants - Summarization tools Apply prompt engineering, Retrieval-Augmented Generation (RAG), and context-aware pipelines to enhance model accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores using Python, Java … experience in Java or Node.js. Hands-on experience with LLMs (e.g., GPT, LLaMA, Claude, Mistral), Transformers, and Diffusion models. Experience with Hugging Face Transformers, LangChain, LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g. … MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or related field. Luxoft is committed to fostering a diverse and inclusive workplace. We show fairness to all throughout our More ❯

Diffusion models) for real-world enterprise use cases. Build and fine-tune LLM-based applications such as: - Chatbots - Document Q&A systems - Report generators - Code assistants - Summarization tools Apply prompt engineering, Retrieval-Augmented Generation (RAG), and context-aware pipelines to enhance model accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores using Python, Java … experience in Java or Node.js. Hands-on experience with LLMs (e.g., GPT, LLaMA, Claude, Mistral), Transformers, and Diffusion models. Experience with Hugging Face Transformers, LangChain, LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g. … MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or related field. Luxoft is committed to fostering a diverse and inclusive workplace. We show fairness to all throughout our More ❯

Prompt Engineer - LLMs & Responsible AI £775/day Inside IR35 | End of FY | Hands-on Prompt Engineer needed for a government AI programme. Design and evaluate prompts across multiple LLMs (ChatGPT, Claude) to support policy and business use cases. The Role Craft and refine prompts using advanced techniques (ReAct, Chain-of-Thought, ART) Build multi-agent workflows and … context-aware LLM systems Advise on responsible AI practices and risk mitigation Collaborate with policy professionals and user researchers Essential Skills Proven prompt engineering experience across multiple LLMs Multi-agent systems and tool-augmented workflows Deep understanding of LLM failure modes and responsible AI Ability to translate technical concepts for non-technical stakeholders The Details Rate: £775/ More ❯

Prompt Engineer - LLMs & Responsible AI £775/day Inside IR35 | End of FY | Hands-on Prompt Engineer needed for a government AI programme. Design and evaluate prompts across multiple LLMs (ChatGPT, Claude) to support policy and business use cases. The Role Craft and refine prompts using advanced techniques (ReAct, Chain-of-Thought, ART) Build multi-agent workflows and … context-aware LLM systems Advise on responsible AI practices and risk mitigation Collaborate with policy professionals and user researchers Essential Skills Proven prompt engineering experience across multiple LLMs Multi-agent systems and tool-augmented workflows Deep understanding of LLM failure modes and responsible AI Ability to translate technical concepts for non-technical stakeholders The Details Rate: £775/ More ❯

HCLTech is a global technology company, home to 219,000+ people across 54 countries, delivering industry-leading capabilities centered on digital, engineering and cloud, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media … 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 expertise … in RAG architecture and evaluation metrics. Proven experience with Vector Databases such as Milvus, Pinecone, or Chroma. Software & Cloud Engineering: Programming & APIs: Expert-level Python and demonstrable experience building production services with FastAPI. Cloud Platform: Mastery of GCP, particularly Vertex AI, Google Kubernetes Engine (GKE), and Cloud Functions. Databases: Strong command of relational databases like PostgreSQL and familiarity with More ❯

HCLTech is a global technology company, home to 219,000+ people across 54 countries, delivering industry-leading capabilities centered on digital, engineering and cloud, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media … 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 expertise … in RAG architecture and evaluation metrics. Proven experience with Vector Databases such as Milvus, Pinecone, or Chroma. Software & Cloud Engineering: Programming & APIs: Expert-level Python and demonstrable experience building production services with FastAPI. Cloud Platform: Mastery of GCP, particularly Vertex AI, Google Kubernetes Engine (GKE), and Cloud Functions. Databases: Strong command of relational databases like PostgreSQL and familiarity with More ❯

for a leading research platform. This is a hands-on role focused on production-grade LLM applications, AI-enabled workflows, and augmented intelligence solutions. You will collaborate closely with engineering, research, analytics, and product teams to drive innovation and deliver impactful solutions. Key Responsibilities: - Data & Retrieval: Build ingestion pipelines for structured and unstructured data; design retrieval-augmented generation (RAG … NLP and recommendation systems; implement metadata and tagging frameworks. - LLM & ML Applications: Develop and maintain ML and LLM models; build LLM apps with LangChain/LangGraph; apply multimodal AI, prompt engineering, fine-tuning, and model optimization; ensure scalable, reliable, and business-aligned AI solutions. - Platform & Operations (MLOps): Deploy and operate services on Azure; implement CI/CD and … Infrastructure as Code; add monitoring, logging, and observability; ensure reliability, fault tolerance, and performance optimization. - Governance & Security: Enforce data privacy, ethical AI practices, and access controls; mitigate risks like prompt injection; establish model versioning, bias mitigation, and reproducibility best practices. - Software Engineering & Collaboration: Write clean, maintainable code; contribute to architecture, POCs, and production services; mentor junior engineers; stay More ❯

for a leading research platform. This is a hands-on role focused on production-grade LLM applications, AI-enabled workflows, and augmented intelligence solutions. You will collaborate closely with engineering, research, analytics, and product teams to drive innovation and deliver impactful solutions. Key Responsibilities: - Data & Retrieval: Build ingestion pipelines for structured and unstructured data; design retrieval-augmented generation (RAG … NLP and recommendation systems; implement metadata and tagging frameworks. - LLM & ML Applications: Develop and maintain ML and LLM models; build LLM apps with LangChain/LangGraph; apply multimodal AI, prompt engineering, fine-tuning, and model optimization; ensure scalable, reliable, and business-aligned AI solutions. - Platform & Operations (MLOps): Deploy and operate services on Azure; implement CI/CD and … Infrastructure as Code; add monitoring, logging, and observability; ensure reliability, fault tolerance, and performance optimization. - Governance & Security: Enforce data privacy, ethical AI practices, and access controls; mitigate risks like prompt injection; establish model versioning, bias mitigation, and reproducibility best practices. - Software Engineering & Collaboration: Write clean, maintainable code; contribute to architecture, POCs, and production services; mentor junior engineers; stay More ❯

relationship extraction. Design and implement intelligent tagging and metadata enrichment frameworks to categorize and organize legal and market data, improving search, discoverability, and insight accuracy. LLM & Machine Learning Application Engineering Design, build, and maintain traditional ML and LLM models and pipelines. Build LLM apps using LangGraph/LangChain: tools/function calling, structured outputs (JSON Schema), agents, and multi … step reasoning. Implement ASR/TTS and multimodal where relevant (e.g., Whisper). Choose customization paths pragmatically: prompt engineering, system prompts, tools, adapters/LoRA, and selective fine-tuning only when needed. Fine-tune and optimise ML models and LLMs to enhance performance, efficiency, and relevance for Chambers’ research, analytics, and product applications. Apply best practices for model … retries/backoff, idempotency, circuit breakers, caching (e.g., Redis/semantic cache), fallbacks and degradations. Governance, Safety & Security Enforce PII handling, data minimization, redaction, access controls, and auditability. Mitigate prompt injection/jailbreak risks; apply content filters/guardrails; track data residency. Establish and drive best practices for model versioning, reproducibility, performance monitoring, bias mitigation, data governance, and ethical More ❯

Responsibilities - Lead end-to-end execution of complex data science projects integrating statistical modelling, machine learning (ML), and deep learning (DL). - Collaborate closely with cross-functional teams (product, engineering, business stakeholders) to deliver data-driven solutions. - Design and conduct hypothesis testing (A/B and multivariate testing) and apply causal inference methodologies. - Perform exploratory data analysis, feature engineering … detecting drift and decay. - Drive best practices for model explainability (e.g., SHAP/LIME) and scalable ML systems (including generative AI, NLP, CV, and recommendation engines). - Partner with engineering teams to ensure robust deployment and adherence to MLOps principles. - Shape and consult on broader data strategy and infrastructure. - Mentor and coach junior team members while staying ahead of … machine learning fundamentals, statistical inference, and model evaluation. - Advanced proficiency in SQL (e.g., PostgreSQL, ELT/ETL) and Python (PyTorch, LightGBM, Scikit-learn). - Experience with modern AI concepts: prompt engineering, embeddings, vector search, etc. - Skilled in managing complex codebases (Git) and working with cloud platforms (GCP, AWS). - Excellent analytical, communication, and organisational skills. Desirable - MS/ More ❯

Responsibilities - Lead end-to-end execution of complex data science projects integrating statistical modelling, machine learning (ML), and deep learning (DL). - Collaborate closely with cross-functional teams (product, engineering, business stakeholders) to deliver data-driven solutions. - Design and conduct hypothesis testing (A/B and multivariate testing) and apply causal inference methodologies. - Perform exploratory data analysis, feature engineering … detecting drift and decay. - Drive best practices for model explainability (e.g., SHAP/LIME) and scalable ML systems (including generative AI, NLP, CV, and recommendation engines). - Partner with engineering teams to ensure robust deployment and adherence to MLOps principles. - Shape and consult on broader data strategy and infrastructure. - Mentor and coach junior team members while staying ahead of … machine learning fundamentals, statistical inference, and model evaluation. - Advanced proficiency in SQL (e.g., PostgreSQL, ELT/ETL) and Python (PyTorch, LightGBM, Scikit-learn). - Experience with modern AI concepts: prompt engineering, embeddings, vector search, etc. - Skilled in managing complex codebases (Git) and working with cloud platforms (GCP, AWS). - Excellent analytical, communication, and organisational skills. Desirable - MS/ More ❯

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 bias toward action and experimentation Nice to Have Experience … deploying AI agents at scale Knowledge of cloud platforms (AWS, GCP, Azure) Background in data engineering, NLP, or ML Ops Why Join Us Work on cutting-edge AI projects with real-world impact Collaborate with a world-class engineering team Flexible remote environment with strong growth potential Competitive salary, equity options, and great benefits If you are interested More ❯

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 bias toward action and experimentation Nice to Have Experience … deploying AI agents at scale Knowledge of cloud platforms (AWS, GCP, Azure) Background in data engineering, NLP, or ML Ops Why Join Us Work on cutting-edge AI projects with real-world impact Collaborate with a world-class engineering team Flexible remote environment with strong growth potential Competitive salary, equity options, and great benefits If you are interested More ❯

stack development and AI system design to complex enterprise environments. This role demands hands-on experience in building and deploying AI/ML solutions, a strong grasp of software engineering principles, and proficiency with cloud platforms. You will play a key role in developing scalable, intelligent systems that drive innovation and deliver measurable business value. Your Responsibilities: Full Stack … with AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). Experience with state-of-the-art LLMs (e.g., GPT series, Llama series, Mistral, Claude), including prompt engineering, fine-tuning, and evaluation. Hands-on experience with core GenAI frameworks (e.g., LangChain, LlamaIndex) and Agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph). Proficiency in MLOps/… MLflow, Kubeflow, Docker, Kubernetes). Strong working knowledge and practical deployment experience on major cloud platforms (AWS, Azure, GCP), including their AI/ML services. Strong foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g. More ❯

stack development and AI system design to complex enterprise environments. This role demands hands-on experience in building and deploying AI/ML solutions, a strong grasp of software engineering principles, and proficiency with cloud platforms. You will play a key role in developing scalable, intelligent systems that drive innovation and deliver measurable business value. Your Responsibilities: Full Stack … with AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). Experience with state-of-the-art LLMs (e.g., GPT series, Llama series, Mistral, Claude), including prompt engineering, fine-tuning, and evaluation. Hands-on experience with core GenAI frameworks (e.g., LangChain, LlamaIndex) and Agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph). Proficiency in MLOps/… MLflow, Kubeflow, Docker, Kubernetes). Strong working knowledge and practical deployment experience on major cloud platforms (AWS, Azure, GCP), including their AI/ML services. Strong foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g. More ❯

Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open-source alternatives … Llama, Mistral). Experience with prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings, and building AI-powered automation solutions that leverage natural language understanding. Appian BPA Platform - Strong experience with Appian low-code platform including process modelling, interface design, expression rules, integration objects, and data modelling. Skilled in building end-to … scraping, and legacy system integration. Ability to assess when RPA vs. API integration vs. AI solutions are most appropriate, and experience building hybrid automation solutions combining multiple technologies. Data Engineering & Pipeline Development - Strong skills in building data pipelines for AI/automation solutions including data extraction, transformation, and loading (ETL). Experience with SQL databases (SQL Server), data validation More ❯

skills in Python, JavaScript, or similar languages. LLM & Agent Expertise: Hands-on experience with LLMs (OpenAI, Anthropic, Mistral) and agent frameworks. Advanced Tools: Familiarity with vector databases, RAG pipelines, prompt engineering, REST APIs, cloud deployment, and containerization (Docker/Kubernetes). Domain Knowledge: Experience in one or more business domains such as finance, housing, operations, or customer service. More ❯

product and our business. Key Responsibilities End-to-End ML Pipeline Ownership: Take charge of the entire ML lifecycle, from conceptualising impactful applications to deployment and monitoring. Data Ingestion & Engineering: Design, build, and maintain robust data ingestion pipelines, including custom web-scraping of agency websites (HTML, PDF) and leveraging APIs where available. Restructure and optimise our PostgreSQL database to … support our evolving data schema. LLM Experimentation & Deployment: Rapidly experiment with and implement LLM-based solutions (from prompt engineering with state-of-the-art APIs to fine-tuning open-source models) to extract, consolidate, and categorise key variables from complex text. Human-in-the-Loop System Design: Design and implement the frameworks and tools that facilitate efficient validation … deploying applications using Large Language Models (e.g., via OpenAI, Anthropic, or open-source models via Hugging Face). Experience building and maintaining data pipelines and a comfort with software engineering best practices (version control, CI/CD, testing). Exceptional communication skills, with the ability to explain technical concepts to non-technical stakeholders and drive alignment. A proactive, curious More ❯

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 knowledge of automation tools including GitHub Actions, Terraform, and … with business process automation (RPA) tools like Appian Workflow orchestration experience (Airflow, Prefect) Ability to build custom automation frameworks using Python or similar languages Full-Stack Development Solid software engineering background with proficiency in Python, JavaScript/TypeScript, Java, or Go Experience with modern frameworks (React, Node.js, Django, FastAPI) Knowledge of microservices architecture, API design (REST, GraphQL) Experience with More ❯

with our architecture, research, analytics, and product teams, you’ll bring creativity and technical expertise to the forefront of our data and technology strategy. This is a hands-on engineering position focused on building and operating production-grade LLM applications on Azure. You’ll work on AI-enabled and augmented intelligence solutions such as retrieval-augmented generation (RAG), agentic … relationship extraction. Design and implement intelligent tagging and metadata enrichment frameworks to categorize and organize legal and market data, improving search, discoverability, and insight accuracy. LLM & Machine Learning Application Engineering Design, build, and maintain traditional ML and LLM models and pipelines . Build LLM apps using LangGraph/LangChain : tools/function calling, structured outputs (JSON Schema), agents, and … multi-step reasoning. Implement ASR/TTS and multimodal where relevant (e.g., Whisper ). Choose customization paths pragmatically: prompt engineering , system prompts, tools, adapters/LoRA, and selective fine-tuning only when needed. Fine-tune and optimize ML models and LLMs to enhance performance, efficiency, and relevance for Chambers’ research, analytics, and product applications. Apply best practices for More ❯

Application Development: Build, fine-tune, and deploy LLM-based applications such as advanced chatbots, summarization tools, document Q&A systems, and code assistants. Advanced Techniques: Apply sophisticated techniques like Prompt Engineering , Retrieval-Augmented Generation (RAG) , and context-aware pipelines to maximize model accuracy and relevance. Integration: Seamlessly integrate deployed AI models with existing enterprise systems, APIs, and data … ML). Expertise in managing model evaluation, drift monitoring, and continuous improvement processes. Strong focus on optimizing inference performance and cost (e.g., model compression, quantization, API optimization). Data Engineering for Generative AI: Experience in preparing and curating diverse training datasets (structured/unstructured text, images, code). Deep knowledge of data preprocessing, tokenization, and embedding generation techniques. Hands More ❯

Application Development: Build, fine-tune, and deploy LLM-based applications such as advanced chatbots, summarization tools, document Q&A systems, and code assistants. Advanced Techniques: Apply sophisticated techniques like Prompt Engineering , Retrieval-Augmented Generation (RAG) , and context-aware pipelines to maximize model accuracy and relevance. Integration: Seamlessly integrate deployed AI models with existing enterprise systems, APIs, and data … ML). Expertise in managing model evaluation, drift monitoring, and continuous improvement processes. Strong focus on optimizing inference performance and cost (e.g., model compression, quantization, API optimization). Data Engineering for Generative AI: Experience in preparing and curating diverse training datasets (structured/unstructured text, images, code). Deep knowledge of data preprocessing, tokenization, and embedding generation techniques. Hands More ❯

to reinvent how internal business systems and data work together. As a Staff Software Engineer (AI/ML) , you’ll be the technical lead for a newly formed AI Engineering function . You’ll build and scale AI-powered backend systems that connect enterprise data platforms (CRM, analytics, and communication tools) with large language models — creating intelligent tools and … deliver high-value AI applications. Mentor engineers and shape the company’s approach to internal AI enablement. What You’ll Bring 7+ years’ experience in backend or full-stack engineering, ideally within a SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and … Hybrid working with a central London office (bike storage and showers available) “Work from Abroad” policy (up to 5 days per year) Pension scheme Collaborative, inclusive, and forward-thinking engineering culture Strong focus on career development and ownership More ❯