1 to 25 of 82 Remote Prompt Engineering Jobs

charge in developing LLM-powered agents, assistants, custom tooling for developers, and AI automation frameworks. This is a hands-on role that combines software engineering, ML experimentation, and AI product innovation. You’ll be empowered to shape agent architecture, explore cutting-edge frameworks like LangChain and LlamaIndex, and be … frameworks like LangChain, LangGraph, LlamaIndex, etc. AI Assistant & Tool Innovation: Build AI-powered agents, assistants, and tools to streamline workflows and enhance productivity. AI Engineering: Build, test, and optimize generative AI features using OpenAI, Claude, Gemini, and fine-tuned custom models. Prompt Engineering: Craft, iterate, and test … prompt strategies to improve accuracy and responsiveness. Cross-Functional Collaboration: Partner with engineering, product, and QA teams to deliver agent-led automation solutions. Prototyping & R&D: Drive rapid prototyping of LLM-based solutions, develop PoCs, and validate ideas. Responsible AI: Promote safe, ethical, and transparent AI practices. AI More ❯

data platform which can create and deliver analytics to colleagues and deliver regulatory reporting Ingest and transform data from multiple systems, modelling data and engineering data marts to create reusable data assets To create a self-service BI platform, enabling colleagues across Rentokil Initial to get value from data … data science platform that enables Rentokil to derive huge value from AI, through machine learning to gen AI and beyond Key tasks: AI Data Engineering: Design, build, operate, and deploy real-time data pipelines at scale using AI techniques and best practices. Support Rentokil's AI R&D efforts … by applying advanced data warehousing, data science, and data engineering technologies. Aim for automation to enable a faster time-to-market and better reusability of new AI initiatives. Collaboration: Work in tandem with the AI team to collect, create, curate, and maintain high-quality AI datasets. Ensure alignment of More ❯

data platform which can create and deliver analytics to colleagues and deliver regulatory reporting. Ingest and transform data from multiple systems, modelling data and engineering data marts to create reusable data assets. To create a self-service BI platform, enabling colleagues across Rentokil Initial to get value from data. … data science platform that enables Rentokil to derive huge value from AI, through machine learning to gen AI and beyond. Key tasks: AI Data Engineering: Design, build, operate, and deploy real-time data pipelines at scale using AI techniques and best practices. Support Rentokil's AI R&D efforts … by applying advanced data warehousing, data science, and data engineering technologies. Aim for automation to enable a faster time-to-market and better reusability of new AI initiatives. Collaboration: Work in tandem with the AI team to collect, create, curate, and maintain high-quality AI datasets. Ensure alignment of More ❯

computing, 5G communications, automotive, aerospace industrial and health. Job Title: Sr Principal Software Engineer Location: Cambridge, United Kingdom OR Cork, Ireland Reports to: Software Engineering Group Director Job Overview: We are seeking a Sr Principal Software Engineer (Team Lead) to join our team responsible for developing and maintaining AI … future of Cadence's innovative solutions. Job Responsibilities: Lead and mentor a team of software engineers working on infrastructure that supports, for example, inference, prompt engineering, fine-tuning, and model quantization. Collaborate with product managers and cross-functional teams to deliver software systems and libraries to deliver AI … technologies in AI and machine learning. Foster an environment of continuous learning, innovation, and technical excellence. Job Qualifications: Good degree in Computer Science, Electrical Engineering, or related technical field. Strong programming skills in C, C++, Python. Knowledge of AI/ML concepts and frameworks. Willingness to learn multiple programming More ❯

consumer, hyperscale computing, 5G communications, automotive, aerospace industrial and health. Job Title: Software Engineer II Location: Cambridge, United Kingdom OR Cork, Ireland Reports to: Engineering Group Director Job Overview: We are seeking a Junior Software Engineer to join our team responsible for developing and maintaining AI infrastructure across Cadence … helping to shape the future of Cadence's innovative solutions. Job Responsibilities: Contribute to the development of AI infrastructure that supports, for example, inference, prompt engineering, fine-tuning, and model quantization. Assist in developing software systems and libraries to deliver AI-driven functionality across the whole Cadence organization. … technologies in AI and machine learning. Document technical processes and developments for knowledge sharing across teams. Job Qualifications: Good degree in Computer Science, Electrical Engineering, or related technical field. Strong programming skills in C, C++, Python. Knowledge of AI/ML concepts and frameworks. Willingness to learn multiple programming More ❯

as OpenAI GPT or Anthropic Claude. Design and implement RAG pipelines for scalable, real-time applications leveraging vector databases like Pinecone, Weaviate, Opensearch. Develop prompt engineering strategies to optimize model outputs for specific use cases. Design and deploy scalable ML models that integrate with existing systems. End-to … End ML Pipeline: Architect, train, and deploy machine learning pipelines for NLP and multimodal AI solutions. Conduct data preprocessing, feature engineering, and exploratory data analysis for training datasets. Optimize embeddings for semantic search and document retrieval tasks. Model Deployment & Optimization: Deploy ML models in production environments using cloud platforms More ❯

like, search, summarization, recommendations, and Q&A. Design Retrieval-Augmented Generation (RAG) pipelines to ground LLMs with customer-specific context and data. Develop & educate prompt strategies and support the design of abstractions for prompt reuse and testing. Deploy and optimize models in production, with a focus on speed … . Evaluate and monitor models, ensuring accuracy, relevance, and fairness. Embed AI guardrails for secure, responsible, and explainable AI systems. Collaborate cross-functionally with engineering, product, and customer teams to deliver high-impact features and help the team grow through knowledge sharing, documentation, and pairing on complex problems. What … You Bring 5+ years in software/ML engineering developing & deploying production systems. Strong commitment to testing, validation, and observability-especially in systems where correctness can be fuzzy. Experience designing robust, testable, and observable GenAI systems-where correctness, fallback behavior, and evaluation are essential. Deep hands-on experience in More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯

is a significant advantage. Your hands-on experience with advanced LLM features such as Function Calling and API integrations, Vector Embeddings and semantic search, Prompt Engineering and prompt optimization or RAG (Retrieval-Augmented Generation). Demonstrated experience in cloud-native environments and proficiency in diverse architectural paradigms More ❯