14 of 14 Pinecone Jobs in the UK

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 ❯

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 More ❯

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 More ❯

tuning. Proficiency in Python, PyTorch, TensorFlow, and Hugging Face Transformers. Hands-on experience with LangChain, CrewAI, and LangFlow (bonus points for deep expertise). Strong understanding of vector databases (Pinecone, Weaviate, FAISS) and embedding models. Experience building production-ready AI products, ensuring scalability and reliability. Deep knowledge of prompt engineering, tokenization strategies, and data augmentation for LLMs. Familiarity with ML More ❯

problem solving and collaboration skills. Nice to Have Skills: Experience with LangChain, Semantic Kernel, or Retrieval Augmented Generation (RAG) patterns in Azure. Knowledge of vector databases (Azure Cognitive Search, Pinecone, Weaviate, etc.) for LLM applications. Exposure to real time streaming & event driven architectures (Event Hub, Kafka). Familiarity with KubeFlow, MLflow, or other MLOps orchestration frameworks. Personal High analytical skills More ❯

and Kubeflow. ?? Youll Bring Strong Python skills and experience with LangChain, Transformers, Hugging Face. Solid grasp of LLM behavior, prompt optimization, and data engineering. Familiarity with vector databases (FAISS, Pinecone, ChromaDB). Hands-on with Linux, Bash/Powershell scripting, cloud environments. Creative problem-solver with excellent communication and collaboration skills. Curious, adaptable, and passionate about staying at the edge More ❯

with relevant technologies such as OpenAI, LangChain/LangGraph, LlamaIndex Experience with Hugging Face and LoRA/QLoRA for fine-tuning Experience with RAG & Vector DBs eg. FAISS, Weaviate, Pinecone Any experience of MLOps with MLFlow, AWS (SageMaker), CI/CD (GitHub Actions) or similar would be a benefit to an application The employer is well known not only for More ❯

experience building retrieval-augmented AI search solutions. LLM Fine-Tuning: Experience fine-tuning models for domain-specific performance and optimizing inference speed. Vector Search: Knowledge of DataStax Vector Search, Pinecone, FAISS, or Weaviate. Deployment: Familiarity with Docker, Kubernetes, and CI/CD pipelines (GitHub Actions, Jenkins, or similar). Security & Authentication: Strong understanding of OAuth2, JWT, and API security best More ❯

Data Acumen: Solid understanding of data requirements for machine learning models, including feature engineering, data validation, and dataset versioning. Vector Database Experience: Practical experience working with vector databases (e.g., Pinecone, Milvus, Chroma) for embedding storage and retrieval. Generative AI Familiarity: Understanding of data paradigms for LLMs, RAG architectures, and how data pipelines support fine-tuning or pre-training. MLOps Principles More ❯

Experience building with generative AI applications in production environments. Expertise with microservices architecture and RESTful APIs. Solid understanding of database technologies such as PostgreSQL and vector databases as Elastic, Pinecone, Weaviate, or similar. Familiarity with cloud platforms (AWS, GCP, etc.) and containerized environments (Docker, Kubernetes). Familiarity with MCP, devtools, AI agents, or contributed to open source You are committed More ❯

Practical experience with memory frameworks like Mem0 or Letta. Understanding and production experience with traditional RAG and agentic RAG architectures. Strong grasp of embedding systems and vector databases (e.g., Pinecone, Weaviate, FAISS). Production experience with LiveKit or similar audio/video platforms. What You’ll Be Doing: Building and deploying scalable AI features, agents, and services powered by LLMs. More ❯

end frameworks (e.g. React, Next.js, React Native) and back-end systems (e.g. Node.js, Python). AI integrations: Hands-on experience integrating AI tools and services (e.g. OpenAI, Anthropic, LangChain, Pinecone, speech/video APIs). Prototype mindset: You've built MVPs in fast-moving environments and are comfortable with ambiguity. Cloud/data experience: Familiar with AWS, GCP, or Firebase More ❯

We're partnering with an established AI consultancy seeking a Contract Machine Learning Engineer (NLP) to lead an NLP project for a major utilities client. You'll develop and deploy transformer models, LLMs, and RAG pipelines to extract insights from More ❯

Experience working with LLMs and prompt design (ideally OpenAI or similar) An understanding of structured data (SQL, JSON, CSV) and unstructured text (PDFs Exposure to RAG architectures and vector stores (such as FAISS, Pinecone...) Prompt Engineer (AI & Data) – Could suit More ❯