1 to 25 of 72 Prompt Engineering Jobs in London

GenAI Prompt Engineer Certain Advantage are recruiting on behalf of our globally renowned brand for a GenAI Prompt Engineer in London, initially until 31st Dec. This will need 2-3 days working onsite in Central London. The Role & Responsibilities Design, test, and refine prompts to optimise the performance of LLMs across various tasks (e.g., summarisation, Q&A, content … generation, reasoning). Develop prompt libraries and reusable templates for internal and external use cases. Collaborate with cross-functional teams to integrate prompt engineering into product workflows and AI pipelines. Analyse model outputs and user feedback to iteratively improve prompt effectiveness and reliability. Stay current with the latest research and advancements in prompt engineering … Bachelors or Masters degree in Computer Science, Artificial Intelligence, Linguistics, or a related field. Commercial years of experience in AI/ML, with at least 1 year focused on prompt engineering or working with LLMs. Strong understanding of NLP, transformer models, and generative AI principles. Hands-on experience with platforms such as OpenAI, Anthropic, Cohere, or Hugging Face. More ❯

pre-sales technical design by analyzing customer needs and recommending solutions aligned with Agentforce capabilities and integration with external agent frameworks. Shape best practices around generative AI, agent interoperability, prompt engineering, Data Cloud, and cross-platform integrations. Collaborate with AEs and SEs to build hands-on prototypes and demos using Agentforce and integrated external agents. Develop thought leadership … these systems collaborate. Enable Conversational & Background Agents - Use Agentforce Studio and Agent Builder to configure chat and background agents; integrate with external channels including voice via hyperscaler APIs. Drive Prompt Engineering & Lifecycle Strategy - Lead prompt design, testing, monitoring, and iteration; define agent lifecycle best practices from development through refinement. Build Hands-On Demos & Prototypes - Co-create quick … knowledge bases, and FAQ resources to rapidly resolve internal technical inquiries, empowering specialist teams to handle technical discussions confidently. Requirements Technical Pre-Sales/Consulting: Several years in solutions engineering, architecture, or technical consulting, ideally in B2B SaaS. Hands-on experience with Salesforce Agentforce Strong understanding of external agent ecosystems and interoperability Proven track record in prompt engineering More ❯

Job Title: Prompt Engineer Contract: 6 Months Location: London (Hybrid) Overview We are seeking a Prompt Engineer to join our team on a six-month contract. This role is ideal for someone passionate about language models, prompt design, and practical GenAI applications. You'll play a key role in optimizing how large language models (LLMs) are used … across various workflows, helping drive innovation and performance through effective prompt engineering. Roles and Responsibilities Design, test, and refine prompts to optimize LLM performance across diverse tasks and domains. Build and maintain a library of prompt templates and reusable components. Collaborate with engineers, analysts, and business teams to embed prompt engineering into broader AI workflows. Analyse … model outputs and user feedback to iteratively improve prompt accuracy, consistency, and relevance. Monitor developments in the fields of prompt engineering, generative AI, and LLM capabilities to inform internal practices. Contribute to documentation, internal guidelines, and knowledge-sharing initiatives related to prompt engineering. Required Background Demonstrable experience with prompt writing and LLM fine-tuning. Skilled More ❯

grow, we are always looking for extraordinary talent who want to be part of this our success and help drive our limitless ambitions. About the role The AI & ML Engineering team accelerates the adoption of AI across the business, championing innovation while ensuring our machine learning products are robust, scalable, and cost-efficient. We enable teams to solve problems … using existing AI tools where possible and build custom solutions when needed. Our remit spans AI enablement, agentic systems development, and "conventional" ML engineering for non-GenAI applications like recommender systems. This is a highly visible, hands-on engineering role with a strong focus on pragmatic, production-ready solutions that drive real business impact. This role and team … collaborate closely with the wider Data team (Data Science, Core Data Engineering) and the wider organisation (Technology, Legal, Information Security) to onboard/develop & scale AI applications. As AI & ML Engineer you will be responsible for The role covers the full AI/ML engineering lifecycle, from discovery to deployment and monitoring. Responsibilities include Partnering with stakeholders to More ❯

solutions that automate processes, improve speed, and enhance decision-making. Role Responsibilities As a Senior AI Applied Engineer, you will: Build & Deploy AI-Driven Software SolutionsYou'll blend software engineering and AI expertise to develop scalable, production-grade systems leveraging generative AI and agentic automation. Own Project-Based Work Across Diverse DomainsYou'll tackle varied engagements, ranging in length … internal teams, translating client needs into technical solutions, requiring strong communication and stakeholder-facing capabilities. Leverage Generative AI ExpertiseUtilise your hands-on experience with generative AI tools or workflows, prompt engineering, LLM integration, or similar, but the emphasis is on engineering implementation and delivery. Who We're Looking For Essential Qualifications & Experience Strong software engineering background … with deep experience deploying scalable systems At least some commercial experience using Generative AI exposure (e.g., LLMs, agentic interfaces, prompt engineering) but primarily strong engineering fluency Comfortable with project-based, delivery-focused work, even if no prior consultancy experience Confident in client-facing situations and able to collaborate effectively with non-technical stakeholders Reliable, consistent work history More ❯

frameworks, carving out solutions specific to client needs and driving efficiencies and business benefits. In the process, you will also be collaborating and working closely with business, product and engineering teams, across geographies. You will also guide the teams technically. You will be part of a learning culture, where teamwork and collaboration are encouraged, excellence is rewarded, and diversity … is respected and valued. Required Experience of architecting and solutioning in Gen AI, Agentic AI, classic ML and automation space Experience and good understanding of Prompt engineering, RAG pipelines, Supervised/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases … Evaluate and select appropriate models, frameworks, and orchestration tools. Ability to transform business problems into analytical solutions, fostering cross functional collaboration between various stakeholders like business, data science and engineering and building integrated solutions Strong articulation, stakeholder management and consulting skills Experience working on client proposals – RFI/RFP/Orals stages Experience in solution presentations to client stakeholders More ❯

frameworks, carving out solutions specific to client needs and driving efficiencies and business benefits. In the process, you will also be collaborating and working closely with business, product and engineering teams, across geographies. You will also guide the teams technically. You will be part of a learning culture, where teamwork and collaboration are encouraged, excellence is rewarded, and diversity … is respected and valued. Required Experience of architecting and solutioning in Gen AI, Agentic AI, classic ML and automation space Experience and good understanding of Prompt engineering, RAG pipelines, Supervised/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases … Evaluate and select appropriate models, frameworks, and orchestration tools. Ability to transform business problems into analytical solutions, fostering cross functional collaboration between various stakeholders like business, data science and engineering and building integrated solutions Strong articulation, stakeholder management and consulting skills Experience working on client proposals – RFI/RFP/Orals stages Experience in solution presentations to client stakeholders More ❯

frameworks, carving out solutions specific to client needs and driving efficiencies and business benefits. In the process, you will also be collaborating and working closely with business, product and engineering teams, across geographies. You will also guide the teams technically. You will be part of a learning culture, where teamwork and collaboration are encouraged, excellence is rewarded, and diversity … is respected and valued. Required Experience of architecting and solutioning in Gen AI, Agentic AI, classic ML and automation space Experience and good understanding of Prompt engineering, RAG pipelines, Supervised/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases … Evaluate and select appropriate models, frameworks, and orchestration tools. Ability to transform business problems into analytical solutions, fostering cross functional collaboration between various stakeholders like business, data science and engineering and building integrated solutions Strong articulation, stakeholder management and consulting skills Experience working on client proposals – RFI/RFP/Orals stages Experience in solution presentations to client stakeholders More ❯

frameworks, carving out solutions specific to client needs and driving efficiencies and business benefits. In the process, you will also be collaborating and working closely with business, product and engineering teams, across geographies. You will also guide the teams technically. You will be part of a learning culture, where teamwork and collaboration are encouraged, excellence is rewarded, and diversity … is respected and valued. Required Experience of architecting and solutioning in Gen AI, Agentic AI, classic ML and automation space Experience and good understanding of Prompt engineering, RAG pipelines, Supervised/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases … Evaluate and select appropriate models, frameworks, and orchestration tools. Ability to transform business problems into analytical solutions, fostering cross functional collaboration between various stakeholders like business, data science and engineering and building integrated solutions Strong articulation, stakeholder management and consulting skills Experience working on client proposals – RFI/RFP/Orals stages Experience in solution presentations to client stakeholders More ❯

LLM-based applications in secure, production-ready environments Integrate front-end components with AI models and back-end logic to deliver seamless, end-to-end functionality Perform data and prompt engineering to optimize model performance and outputs Optimize applications for performance, scalability, and security Collaborate cross-functionally with engineering, product, and AI teams to deliver high-impact … databases, including PostgreSQL, Redis, or MongoDB Solid understanding of containerised microservice infrastructure Working knowledge of Kubernetes, security best practices, and cloud platforms (AWS, GCP, or Azure) Desirable: Experience with prompt engineering, Retrieval-Augmented Generation (RAG), and graph databases Familiarity with multi-agent LLM systems and agentic platforms (e.g., AutoGen, CrewAI), and experience deploying LLM-based applications Experience with … LLM applications that deliver measurable business value Maintain low latency, high system uptime, and strong performance across APIs and AI services Drive fast, iterative collaboration across product, design, and engineering teams Continuously improve the platform through testing, research, and integration of modern AI and software practices Why Join Us? Join a mission-driven, high-growth AI startup shaping the More ❯

LLM-based applications in secure, production-ready environments Integrate front-end components with AI models and back-end logic to deliver seamless, end-to-end functionality Perform data and prompt engineering to optimize model performance and outputs Optimize applications for performance, scalability, and security Collaborate cross-functionally with engineering, product, and AI teams to deliver high-impact … databases, including PostgreSQL, Redis, or MongoDB Solid understanding of containerised microservice infrastructure Working knowledge of Kubernetes, security best practices, and cloud platforms (AWS, GCP, or Azure) Desirable: Experience with prompt engineering, Retrieval-Augmented Generation (RAG), and graph databases Familiarity with multi-agent LLM systems and agentic platforms (e.g., AutoGen, CrewAI), and experience deploying LLM-based applications Experience with … LLM applications that deliver measurable business value Maintain low latency, high system uptime, and strong performance across APIs and AI services Drive fast, iterative collaboration across product, design, and engineering teams Continuously improve the platform through testing, research, and integration of modern AI and software practices Why Join Us? Join a mission-driven, high-growth AI startup shaping the More ❯

stack product features across front-end, back-end, AI, devops and MLops. Create systems that can improve over time based on feedback from humans in the loop, such as prompt optimisation and fine-tuning. Explore applications of the latest developments of AI to enhance our product features, explainability, speed and accuracy. What You'll Do Front-end: Build interfaces … build systems that are faster, more thorough and more accurate than humans. Devops: Ensure that we maintain fast deployments and good CI/CD practices to maximise development velocity. Prompt Engineering: Develop and test prompts to optimise model performance and deliver high-quality outputs. Model Fine-Tuning: Adapt and fine-tune large language models and vision models for … specific use cases. Custom Model Training: Train and deploy bespoke AI models tailored to solve unique compliance challenges. Collaboration: Work closely with the engineering and product teams to translate customer needs into actionable AI solutions. Iterate: Continuously iterate on our AI systems to improve performance, reduce errors, and deliver value to customers. Our Culture Deliver Value Fast - Speed + More ❯

are seeking a Senior Data Scientist to provide oversight and hands-on expertise for the build of an AI Agent within an automated insurance workflow . The project involves prompt engineering of LLMs, API development and integration , and deploying solutions in line with a defined solution design. The successful contractor will also provide day-to-day oversight and … Data Scientist on the project. Role & Responsibilities The Senior Data Scientist will be responsible for: Designing, building, and deploying AI Agent solutions within an insurance workflow. Developing and refining prompt engineering approaches for large language models (LLMs). Building, testing, and integrating API calls to meet solution design specifications. Deploying containerised applications in Azure (or other cloud environments … evaluation reports and recommendations for next-phase development. Skills & Experience Essential: Strong expertise in Python for AI/ML development. Hands-on experience with Large Language Models (LLMs) and prompt engineering. Experience deploying solutions in Azure (or equivalent cloud platforms). Demonstrated ability to deliver AI/ML solutions from design through to deployment. Proven ability to provide technical More ❯

practices for applied ML in the business, bringing your curiosity and drive to help us move fast while building robust foundations Apply modern LLM techniques to business problems: Use prompt-engineering strategies to get consistent, accurate results Explore augmentation methods (e.g. combining LLMs with our own data via embeddings or retrieval) Run fine-tuning experiments to adapt general … PyTorch, Hugging Face, scikit-learn) You've worked with large language models (LLMs) - not just calling APIs, but experimenting with different ways to get the best out of them: Prompt engineering (designing effective prompts, chaining prompts) Fine-tuning or instruction tuning models for specific tasks Embedding-based augmentation (using vector search to give LLMs access to external knowledge More ❯

and evaluate robust AI agent systems and workflows using large language models [LLMs] and modern frameworks [e.g., LangChain, LangGraph]. Own the full LLM application lifecycle: from prototyping and prompt engineering to deployment, monitoring, and maintenance in production. Architect and implement efficient Retrieval-Augmented Generation [RAG] systems utilizing vector databases. Collaborate with cross-functional teams to define requirements … measure and improve the performance, reliability, and safety of our AI agents. Optimize AI systems for scalability, latency, and cost-effectiveness. Required Qualifications Bachelor’s in Computer Science, Electrical Engineering, AI Engineering, or related field. At least 5 years of experience in machine learning or software engineeering with a focus on AI 1+ years of hands-on experience More ❯

and evaluate robust AI agent systems and workflows using large language models [LLMs] and modern frameworks [e.g., LangChain, LangGraph]. Own the full LLM application lifecycle: from prototyping and prompt engineering to deployment, monitoring, and maintenance in production. Architect and implement efficient Retrieval-Augmented Generation [RAG] systems utilizing vector databases. Collaborate with cross-functional teams to define requirements … measure and improve the performance, reliability, and safety of our AI agents. Optimize AI systems for scalability, latency, and cost-effectiveness. Required Qualifications Bachelor’s in Computer Science, Electrical Engineering, AI Engineering, or related field. At least 5 years of experience in machine learning or software engineeering with a focus on AI 1+ years of hands-on experience More ❯

and evaluate robust AI agent systems and workflows using large language models [LLMs] and modern frameworks [e.g., LangChain, LangGraph]. Own the full LLM application lifecycle: from prototyping and prompt engineering to deployment, monitoring, and maintenance in production. Architect and implement efficient Retrieval-Augmented Generation [RAG] systems utilizing vector databases. Collaborate with cross-functional teams to define requirements … measure and improve the performance, reliability, and safety of our AI agents. Optimize AI systems for scalability, latency, and cost-effectiveness. Required Qualifications Bachelor’s in Computer Science, Electrical Engineering, AI Engineering, or related field. At least 5 years of experience in machine learning or software engineeering with a focus on AI 1+ years of hands-on experience More ❯

and evaluate robust AI agent systems and workflows using large language models [LLMs] and modern frameworks [e.g., LangChain, LangGraph]. Own the full LLM application lifecycle: from prototyping and prompt engineering to deployment, monitoring, and maintenance in production. Architect and implement efficient Retrieval-Augmented Generation [RAG] systems utilizing vector databases. Collaborate with cross-functional teams to define requirements … measure and improve the performance, reliability, and safety of our AI agents. Optimize AI systems for scalability, latency, and cost-effectiveness. Required Qualifications Bachelor’s in Computer Science, Electrical Engineering, AI Engineering, or related field. At least 5 years of experience in machine learning or software engineeering with a focus on AI 1+ years of hands-on experience More ❯

most. Connect to your opportunity We are seeking to hire experienced Lead GenAI System Architect(s) in the AI Institute, which is a centre of excellence in Deloitte's Engineering, AI & Data service offering. You will be working with clients and other third parties, as well as Deloitte teams from across the Firm. You will have the opportunity to … shape, define, and deliver transformative AI and Generative AI strategies aligned with business goals. Lead the design, development, and implementation of advanced AI pipelines, encompassing data acquisition, preprocessing, feature engineering, model development, evaluation, and secure deployment at scale. Oversee the development and operationalisation of state-of-the-art AI models, including large language models (LLMs), diffusion models, and other … experience in some of the following: Education & Experience PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related discipline preferred. Outstanding candidates with strong quantitative, computer science, or engineering backgrounds in a related field will also be considered. Extensive experience designing, developing, and deploying enterprise-grade AI/ML solutions, including experience managing technical teams and stakeholder relationships. 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 ❯

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 ❯

AI solutions end-to-end, from the idea and data exploration phase to deployment and monitoring, balancing cutting-edge techniques with pragmatism to deliver measurable impact. • Apply strong software engineering principles, such as modularity, testing, code reviews, CI/CD and observability, to ensure AI systems are reliable, maintainable, production-ready and can be readily adapted to future developments. … classical ML and NLP techniques, LLM-based solutions, and agentic solutions to balance trade-offs between speed, cost, complexity, interpretability, and performance. • Collaborate closely with product, design, and other engineering teams to scope work, align on success metrics, and incrementally ship improvements in user-facing features powered by AI. • Document system architectures and decision rationale early and clearly, enabling … wide Slack channels, Show and Tell presentations and technical deep-dives. What experience you'll need to be successful • A master's degree or above in Computer Science, Electrical Engineering or a related field. • 5+ years of experience building AI/ML systems in production environments, including ownership of key lifecycle stages: data collection, modeling, evaluation, deployment, and monitoring. More ❯

services that support AI agent interaction. Implement APIs, contracts, and metadata aligned with the Model Context Protocol. Develop internal tooling that supports AI-native workflows. Establish best practices for prompt engineering, model fine-tuning, and evaluation. Stay current with developments in AI/ML and apply relevant research to product needs. Participate in design reviews, code reviews, and … incident response. Qualifications Bachelor’s degree in Computer Science, Engineering, or equivalent. Required Skills Software development experience with proven expertise in at least one programming 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 More ❯