Central London, London, United Kingdom Hybrid / WFH Options
Staffworx Limited
custom LLM integrations). Exposure to AI ethics, data privacy, and compliance regulations. Prior experience in multi-agent systems or autonomous AI workflows. Hands-on experience with vector databases (Pinecone, Weaviate, FAISS) and AI embeddings. Remote WorkingSome remote working CountryUnited Kingdom LocationWC1 Job TypeContract or Permanent Start DateApr-Jul 25 Duration9 months initial or permanent Visa RequirementApplicants must be eligible More ❯
/or LLM-powered applications in production environments. Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers , or TensorFlow. Experience with vector search tools (e.g., FAISS, Pinecone, Weaviate) and retrieval frameworks (e.g., LangChain, LlamaIndex). Hands-on experience with fine-tuning and distillation of large language models. Comfortable with cloud platforms (Azure preferred), CI/CD tools More ❯
RAG) for augmenting LLMs with domain-specific knowledge. Prompt engineering and fine-tuning for tailoring model behavior to business-specific contexts. Use of embedding stores and vector databases (e.g., Pinecone, Redis, Azure AI Search) to support semantic search and recommendation systems. Building intelligent features like AI-powered chatbots , assistants , and question-answering systems using LLMs and conversational agents. Awareness of More ❯
RAG) for augmenting LLMs with domain-specific knowledge. Prompt engineering and fine-tuning for tailoring model behavior to business-specific contexts. Use of embedding stores and vector databases (e.g., Pinecone, Redis, Azure AI Search) to support semantic search and recommendation systems. Building intelligent features like AI-powered chatbots , assistants , and question-answering systems using LLMs and conversational agents. Awareness of More ❯
RAG) for augmenting LLMs with domain-specific knowledge. Prompt engineering and fine-tuning for tailoring model behavior to business-specific contexts. Use of embedding stores and vector databases (e.g., Pinecone, Redis, Azure AI Search) to support semantic search and recommendation systems. Building intelligent features like AI-powered chatbots , assistants , and question-answering systems using LLMs and conversational agents. Awareness of More ❯
and fine-tune SLMs/LLMs using domain-specific data (e.g., ITSM, security, operations) • Design and optimize Retrieval-Augmented Generation (RAG) pipelines with vector DBs (e.g., FAISS, Chroma, Weaviate, Pinecone) • Develop agent-based architectures using LangGraph, AutoGen, CrewAI, or custom frameworks • Integrate AI agents with enterprise tools (ServiceNow, Jira, SAP, Slack, etc.) • Optimize model performance (quantization, distillation, batching, caching) • Collaborate … and attention mechanisms • Experience with LangChain, Transformers (HuggingFace), or LlamaIndex • Working knowledge of LLM fine-tuning (LoRA, QLoRA, PEFT) and prompt engineering • Hands-on experience with vector databases (FAISS, Pinecone, Weaviate, Chroma) • Cloud experience on Azure, AWS, or GCP (Azure preferred) • Experience with Kubernetes, Docker, and scalable microservice deployments • Experience integrating with REST APIs, webhooks, and enterprise systems (ServiceNow, SAP More ❯
in Python, with expertise in using frameworks like Hugging Face Transformers, LangChain, OpenAI APIs, or other LLM orchestration tools. A solid understanding of tokenization, embedding models, vector databases (e.g., Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) pipelines. Experience designing and evaluating LLM-powered systems such as chatbots, summarization tools, content generation workflows, or intelligent data extraction pipelines. Deep understanding More ❯
in Python, with expertise in using frameworks like Hugging Face Transformers, LangChain, OpenAI APIs, or other LLM orchestration tools. A solid understanding of tokenization, embedding models, vector databases (e.g., Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) pipelines. Experience designing and evaluating LLM-powered systems such as chatbots, summarization tools, content generation workflows, or intelligent data extraction pipelines. Deep understanding More ❯
monitoring. Full-Stack Integration : Develop APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or Hugging Face Transformers Experience More ❯
monitoring. Full-Stack Integration : Develop APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or Hugging Face Transformers Experience More ❯
monitoring. Full-Stack Integration : Develop APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or Hugging Face Transformers Experience More ❯
City of London, London, Finsbury Square, United Kingdom
The Portfolio Group
monitoring. Full-Stack Integration : Develop APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or Hugging Face Transformers Experience More ❯
day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services on GCP (Vertex AI, Cloud Run, BigQuery) using Terraform and CI/CD best practices … teams to iterate fast and deliver impact Embed security, GDPR compliance, and testing best practices into the core of our stack Tech Stack Python • FastAPI • PostgreSQL + PGVector • Redis • Pinecone/Weaviate • Vertex AI • Cloud Run • Docker • Terraform • GitHub Actions • LangChain/LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background More ❯
day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services on GCP (Vertex AI, Cloud Run, BigQuery) using Terraform and CI/CD best practices … teams to iterate fast and deliver impact Embed security, GDPR compliance, and testing best practices into the core of our stack Tech Stack Python • FastAPI • PostgreSQL + PGVector • Redis • Pinecone/Weaviate • Vertex AI • Cloud Run • Docker • Terraform • GitHub Actions • LangChain/LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background More ❯
day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services on GCP (Vertex AI, Cloud Run, BigQuery) using Terraform and CI/CD best practices … teams to iterate fast and deliver impact Embed security, GDPR compliance, and testing best practices into the core of our stack Tech Stack Python • FastAPI • PostgreSQL + PGVector • Redis • Pinecone/Weaviate • Vertex AI • Cloud Run • Docker • Terraform • GitHub Actions • LangChain/LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background More ❯
machine learning fundamentals , including supervised/unsupervised learning. Experience with cloud environments – ideally Azure , but AWS or GCP also considered. Familiarity with LLMs , prompt engineering , and vector databases (e.g. Pinecone, FAISS). Practical experience building production-ready AI applications. Ability to work on-site in Newcastle in a collaborative, agile environment. A curious mindset, eagerness to learn, and a genuine More ❯
production Hands-on experience with frameworks like LangChain, LangGraph, or custom-built agent orchestration setups Familiarity with LLM APIs (OpenAI, Anthropic, Mistral, etc.), embedding stores, retrieval pipelines (e.g. Weaviate, Pinecone), and eval tooling Comfort building and testing AI workflows that interact with external APIs, file systems, simulations, and toolchains Bonus: interest or experience in robotics, mechanical/aerospace workflows, or More ❯
production Hands-on experience with frameworks like LangChain, LangGraph, or custom-built agent orchestration setups Familiarity with LLM APIs (OpenAI, Anthropic, Mistral, etc.), embedding stores, retrieval pipelines (e.g. Weaviate, Pinecone), and eval tooling Comfort building and testing AI workflows that interact with external APIs, file systems, simulations, and toolchains Bonus: interest or experience in robotics, mechanical/aerospace workflows, or More ❯
production Hands-on experience with frameworks like LangChain, LangGraph, or custom-built agent orchestration setups Familiarity with LLM APIs (OpenAI, Anthropic, Mistral, etc.), embedding stores, retrieval pipelines (e.g. Weaviate, Pinecone), and eval tooling Comfort building and testing AI workflows that interact with external APIs, file systems, simulations, and toolchains Bonus: interest or experience in robotics, mechanical/aerospace workflows, or More ❯
large-scale infrastructure, and modern backend development using Java, Python, Golang, Spring Boot, Flask, and Kubernetes. We focus on integrating RAG-powered LLMs, implementing advanced vector search (FAISS, Milvus, Pinecone), and building scalable and high-performance AI-driven solutions. You Might Be a Good Fit If You: Have deep hands-on software engineering expertise in Java or Python Thrive in … applications using Java, Python, and modern backend frameworks Integrate LLMs into enterprise-scale systems using internal frameworks and libraries Design and implement vector search solutions using FAISS, Milvus, and Pinecone Build scalable APIs and backend services using Spring Boot, Flask, and FastAPI Optimize data storage and retrieval with PostgreSQL/MongoDB and distributed databases Deploy and manage cloud-native applications … Succeed in This Role: Proficiency in Java or Python for backend development Strong knowledge of Spring Boot, Flask, FastAPI, and API design Experience with vector search frameworks (FAISS, Milvus, Pinecone) Expertise in Kubernetes and Docker for scalable deployment Understanding of authentication & security frameworks (Spring Security, SSO) Hands-on experience with PostgreSQL and distributed storage Experience with Maven or Gradle for More ❯
Basingstoke, England, United Kingdom Hybrid / WFH Options
DRE DIGITAL LIMITED
and AI agents 💡 Build APIs, data pipelines, and backend components (mainly Python, FastAPI/Flask) 💡 Deploy microservice-friendly solutions, often in containerised setups (e.g. Docker) 💡 Work with ElasticSearch, Weaviate, Pinecone, and similar tools for vector search 💡 Solve problems, learn fast, and help us push the boundaries What we’re looking for: ✅ Enthusiasm and drive to learn — more important than being More ❯
up. What You’ll Do Build scalable backend microservices in Python (FastAPI) to support RAG workflows and user queries Develop and optimise vector search pipelines using tools like PGVector, Pinecone, or Weaviate Design embedding orchestration and hybrid retrieval mechanisms Implement evaluation frameworks (BLEU, ROUGE, hallucination checks) to monitor answer quality Deploy production systems on GCP (Cloud Run, Vertex AI, BigQuery More ❯
up. What You’ll Do Build scalable backend microservices in Python (FastAPI) to support RAG workflows and user queries Develop and optimise vector search pipelines using tools like PGVector, Pinecone, or Weaviate Design embedding orchestration and hybrid retrieval mechanisms Implement evaluation frameworks (BLEU, ROUGE, hallucination checks) to monitor answer quality Deploy production systems on GCP (Cloud Run, Vertex AI, BigQuery More ❯
up. What You’ll Do Build scalable backend microservices in Python (FastAPI) to support RAG workflows and user queries Develop and optimise vector search pipelines using tools like PGVector, Pinecone, or Weaviate Design embedding orchestration and hybrid retrieval mechanisms Implement evaluation frameworks (BLEU, ROUGE, hallucination checks) to monitor answer quality Deploy production systems on GCP (Cloud Run, Vertex AI, BigQuery More ❯
