1 to 25 of 56 Weaviate Jobs

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 to 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, and 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, and More ❯

Next.js Integrate ML models and embeddings into production pipelines using AWS SageMaker , Bedrock or OpenAI APIs Build support systems for autonomous agents including memory storage, vector search (e.g., Pinecone, Weaviate) and tool registries Enforce system-level requirements for security, compliance, observability and CI/CD Drive PoCs and reference architectures for multi-agent coordination , intelligent routing and goal-directed AI … Experience with secure cloud deployments and production ML model integration Bonus Skills Applied work with multi-agent systems , tool orchestration, or autonomous decision-making Experience with vector databases (Pinecone, Weaviate, FAISS) and embedding pipelines Knowledge of AI chatbot frameworks (Rasa, BotPress, Dialogflow) or custom LLM-based UIs Awareness of AI governance , model auditing, and data privacy regulation (GDPR, DPA, etc. More ❯

Next.js Integrate ML models and embeddings into production pipelines using AWS SageMaker , Bedrock or OpenAI APIs Build support systems for autonomous agents including memory storage, vector search (e.g., Pinecone, Weaviate) and tool registries Enforce system-level requirements for security, compliance, observability and CI/CD Drive PoCs and reference architectures for multi-agent coordination , intelligent routing and goal-directed AI … Experience with secure cloud deployments and production ML model integration Bonus Skills Applied work with multi-agent systems , tool orchestration, or autonomous decision-making Experience with vector databases (Pinecone, Weaviate, FAISS) and embedding pipelines Knowledge of AI chatbot frameworks (Rasa, BotPress, Dialogflow) or custom LLM-based UIs Awareness of AI governance , model auditing, and data privacy regulation (GDPR, DPA, etc. More ❯

Next.js Integrate ML models and embeddings into production pipelines using AWS SageMaker , Bedrock or OpenAI APIs Build support systems for autonomous agents including memory storage, vector search (e.g., Pinecone, Weaviate) and tool registries Enforce system-level requirements for security, compliance, observability and CI/CD Drive PoCs and reference architectures for multi-agent coordination , intelligent routing and goal-directed AI … Experience with secure cloud deployments and production ML model integration Bonus Skills Applied work with multi-agent systems , tool orchestration, or autonomous decision-making Experience with vector databases (Pinecone, Weaviate, FAISS) and embedding pipelines Knowledge of AI chatbot frameworks (Rasa, BotPress, Dialogflow) or custom LLM-based UIs Awareness of AI governance , model auditing, and data privacy regulation (GDPR, DPA, etc. More ❯

Next.js Integrate ML models and embeddings into production pipelines using AWS SageMaker , Bedrock or OpenAI APIs Build support systems for autonomous agents including memory storage, vector search (e.g., Pinecone, Weaviate) and tool registries Enforce system-level requirements for security, compliance, observability and CI/CD Drive PoCs and reference architectures for multi-agent coordination , intelligent routing and goal-directed AI … Experience with secure cloud deployments and production ML model integration Bonus Skills Applied work with multi-agent systems , tool orchestration, or autonomous decision-making Experience with vector databases (Pinecone, Weaviate, FAISS) and embedding pipelines Knowledge of AI chatbot frameworks (Rasa, BotPress, Dialogflow) or custom LLM-based UIs Awareness of AI governance , model auditing, and data privacy regulation (GDPR, DPA, etc. More ❯

modern web frameworks Deep experience with AI/ML frameworks (PyTorch, TensorFlow, Transformers, LangChain) Mastery of prompt engineering and fine-tuning Large Language Models Proficient in vector databases (Pinecone, Weaviate, Milvus) and embedding technologies Expert in building RAG (Retrieval-Augmented Generation) systems at scale Strong experience with MLOps practices and model deployment pipelines Proficient in cloud AI services (AWS SageMaker More ❯

modern web frameworks Deep experience with AI/ML frameworks (PyTorch, TensorFlow, Transformers, LangChain) Mastery of prompt engineering and fine-tuning Large Language Models Proficient in vector databases (Pinecone, Weaviate, Milvus) and embedding technologies Expert in building RAG (Retrieval-Augmented Generation) systems at scale Strong experience with MLOps practices and model deployment pipelines Proficient in cloud AI services (AWS SageMaker More ❯

Python, with expertise in using frameworks like Hugging Face Transformers, LangChain, OpenAI APIs, or other LLM orchestration tools. A solid understanding of tokenisation, 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, summarisation tools, content generation workflows, or intelligent data extraction pipelines. Deep understanding of More ❯

and maintain AI microservices using Docker, Kubernetes, and FastAPI, ensuring smooth model serving and error handling; Vector Search & Retrieval: Implement retrieval-augmented workflows: ingest documents, index embeddings (Pinecone, FAISS, Weaviate), and build similarity search features. Rapid Prototyping: Create interactive AI demos and proofs-of-concept with Streamlit, Gradio, or Next.js for stakeholder feedback; MLOps & Deployment: Implement CI/CD pipelines … tuning LLMs via OpenAI, HuggingFace or similar APIs; Strong proficiency in Python; Deep expertise in prompt engineering and tooling like LangChain or LlamaIndex; Proficiency with vector databases (Pinecone, FAISS, Weaviate) and document embedding pipelines; Proven rapid-prototyping skills using Streamlit or equivalent frameworks for UI demos. Familiarity with containerization (Docker) and at least one orchestration/deployment platform; Excellent communication More ❯

Build 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 … 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, etc. More ❯

and ability to work in a team environment. Preferred Qualifications: Experience working with large-scale AI applications and personalization engines. Familiarity with production-scale vector databases (e.g., QDrant, Pinecone, Weaviate). Understanding of AI model interpretability and ethical AI considerations. Exposure to real-time AI applications and MLOps workflows. Why Join Us? Work alongside industry experts on cutting-edge AI More ❯

data interoperability (FHIR, HL7, CDA). You've built real-time AI applications, including voice AI, speech recognition, or NLP pipelines. You have experience in vector databases (e.g., Pinecone, Weaviate) and retrieval-augmented generation (RAG) architectures. What's in it for you? The opportunity to build and scale AI models in production that directly impact healthcare efficiency. A role where More ❯

vector databases), AutoGPT Data Engineering & ML Pipelines: Apache Airflow, MLflow, Kubeflow, dbt, Prefect Cloud & Deployment Platforms: AWS SageMaker, Azure ML, Google Vertex AI APIs & Orchestration: OpenAI API, Anthropic Claude, Weaviate, FastAPI (for AI applications) MLOps & Experimentation: Weights & Biases, DVC (Data Version Control), Docker, Kubernetes General 2+ years of professional experience in relevant fields. Experience mentoring, coaching, or teaching others in More ❯

and maintain AI microservices using Docker, Kubernetes, and FastAPI, ensuring smooth model serving and error handling; Vector Search & Retrieval: Implement retrieval-augmented workflows: ingest documents, index embeddings (Pinecone, FAISS, Weaviate), and build similarity search features. Rapid Prototyping: Create interactive AI demos and proofs-of-concept with Streamlit, Gradio, or Next.js for stakeholder feedback; MLOps & Deployment: Implement CI/CD pipelines … tuning LLMs via OpenAI, HuggingFace or similar APIs; Strong proficiency in Python; Deep expertise in prompt engineering and tooling like LangChain or LlamaIndex; Proficiency with vector databases (Pinecone, FAISS, Weaviate) and document embedding pipelines; Proven rapid-prototyping skills using Streamlit or equivalent frameworks for UI demos. Familiarity with containerization (Docker) and at least one orchestration/deployment platform; Excellent communication More ❯

and maintain AI microservices using Docker, Kubernetes, and FastAPI, ensuring smooth model serving and error handling; Vector Search & Retrieval: Implement retrieval-augmented workflows: ingest documents, index embeddings (Pinecone, FAISS, Weaviate), and build similarity search features. Rapid Prototyping: Create interactive AI demos and proofs-of-concept with Streamlit, Gradio, or Next.js for stakeholder feedback; MLOps & Deployment: Implement CI/CD pipelines … tuning LLMs via OpenAI, HuggingFace or similar APIs; Strong proficiency in Python; Deep expertise in prompt engineering and tooling like LangChain or LlamaIndex; Proficiency with vector databases (Pinecone, FAISS, Weaviate) and document embedding pipelines; Proven rapid-prototyping skills using Streamlit or equivalent frameworks for UI demos. Familiarity with containerization (Docker) and at least one orchestration/deployment platform; Excellent communication More ❯

and maintain AI microservices using Docker, Kubernetes, and FastAPI, ensuring smooth model serving and error handling; Vector Search & Retrieval: Implement retrieval-augmented workflows: ingest documents, index embeddings (Pinecone, FAISS, Weaviate), and build similarity search features. Rapid Prototyping: Create interactive AI demos and proofs-of-concept with Streamlit, Gradio, or Next.js for stakeholder feedback; MLOps & Deployment: Implement CI/CD pipelines … tuning LLMs via OpenAI, HuggingFace or similar APIs; Strong proficiency in Python; Deep expertise in prompt engineering and tooling like LangChain or LlamaIndex; Proficiency with vector databases (Pinecone, FAISS, Weaviate) and document embedding pipelines; Proven rapid-prototyping skills using Streamlit or equivalent frameworks for UI demos. Familiarity with containerization (Docker) and at least one orchestration/deployment platform; Excellent communication More ❯

and maintain AI microservices using Docker, Kubernetes, and FastAPI, ensuring smooth model serving and error handling; Vector Search & Retrieval: Implement retrieval-augmented workflows: ingest documents, index embeddings (Pinecone, FAISS, Weaviate), and build similarity search features. Rapid Prototyping: Create interactive AI demos and proofs-of-concept with Streamlit, Gradio, or Next.js for stakeholder feedback; MLOps & Deployment: Implement CI/CD pipelines … tuning LLMs via OpenAI, HuggingFace or similar APIs; Strong proficiency in Python; Deep expertise in prompt engineering and tooling like LangChain or LlamaIndex; Proficiency with vector databases (Pinecone, FAISS, Weaviate) and document embedding pipelines; Proven rapid-prototyping skills using Streamlit or equivalent frameworks for UI demos. Familiarity with containerization (Docker) and at least one orchestration/deployment platform; Excellent communication More ❯

assistants): Requirements: • Strong experience with Python and Al/ML libraries (Langchain, TensorFlow, PyTorch) • Experience with frontend frameworks like React or Angular • Knowledge of vector databases (e.g., FAISS, Pinecone, Weaviate) • Familiarity with LLM integrations (e.g., OpenAl, HuggingFace) • Experience building and consuming REST/gRPC APis • Understanding of prompt engineering and RAG architectures • Familiar with cloud platforms (AWS, GCP, or Azure More ❯

Triton, ONNX, FastAPI) and containerized infra (Docker, K8s) Familiarity with MLOps frameworks (MLflow, Kubeflow, SageMaker, Vertex AI) Bonus: exposure to healthcare data formats (FHIR, HL7) or vector databases (Pinecone, Weaviate) You care deeply about model performance, prompt design, and production-grade reliability - not just research experiments. Why This Role Matters You'll ship AI into real-world, high-impact workflows More ❯

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 with RAG More ❯

stakeholders. Strong stakeholder management and project delivery experience across cross-functional teams. Preferred Qualifications Background in AI ethics, fairness, compliance, or regulatory frameworks. Familiarity with Salesforce, vector databases (Pinecone, Weaviate, FAISS), graph-based reasoning, or knowledge graphs. Experience in property maintenance, home services, or customer service automation (not required, but a bonus). Why Join Us? Be a key player More ❯

engineering, and deployment Python Proficiency : Strong skills with ML frameworks (TensorFlow, PyTorch) and LLM tools (Hugging Face, LangChain, OpenAI APIs) Vector Understanding : Solid knowledge of embeddings, vector databases (Pinecone, Weaviate, FAISS), and RAG pipelines NLP Fundamentals : Text preprocessing, language modelling, and semantic similarity Cloud Experience : AWS ecosystem knowledge (SageMaker, Lambda, etc.) Production Ready : ETL pipelines, version control, Agile methodologies It More ❯

for UI/agent interfaces) LLMs & Providers : OpenAI (GPT-4/4o), Anthropic, Mistral, Cohere, Ollama Agent Frameworks : LangChain, AutoGen, CrewAI, Semantic Kernel, LangGraph, Haystack, Guidance Memory & Storage : Chroma, Weaviate, Redis, Pinecone, Postgres, Milvus Execution & Orchestration : Docker, Ray, Celery, Dask Tooling : OpenAI Tools/Functions, LangChain agents, Retrieval-Augmented Generation (RAG), Vector Search DevOps (Optional) : Kubernetes, CI/CD, observability More ❯