pipelines over structured and unstructured health data (EHRs, patient notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely with More ❯
pipelines over structured and unstructured health data (EHRs, patient notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely with More ❯
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 More ❯
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 More ❯
strong plus. Experience with NoSQL databases (e.g., MongoDB, DynamoDB) and in-memory Data Stores (e.g., Redis). Experience with Vector DBs (e.g., Qdrant, FAISS, Pinecone) is a strong plus. Familiarity with building low latency, high availability, and high throughput systems. Familiarity with Docker, CI/CD pipelines, and GCP. Ability More ❯
problem-solving skills and ability to work in a fast-paced, agile environment. Nice-to-Have Skills: Hands-on experience with Vector Databases (FAISS, Pinecone, Weaviate, PGVector, etc.). Experience fine-tuning LLMs for domain-specific applications. Knowledge of data privacy, governance, and compliance in AI-driven systems. Previous work More ❯
and improving developer workflows. Excellent communication and collaboration skills in a remote-first environment. Experience contributing to open-source AI projects. Experience with LangChain, Pinecone, or similar AI frameworks/infrastructure. Past experience building AI features into developer platforms or tools. Benefits Our entire company is distributed, so we take More ❯
Develop and optimise RAG pipelines using LangChain, LlamaIndex, or Haystack. Build ingestion workflows (OCR, chunking, embedding, semantic search) and integrate with vector databases (FAISS, Pinecone, Qdrant). Ensure seamless integration of GenAI services into business workflows, prioritising security, scalability, and compliance. Collaborate with cross-functional teams (data scientists, architects, engineers More ❯
london, south east england, United Kingdom Hybrid / WFH Options
twentyAI
Develop and optimise RAG pipelines using LangChain, LlamaIndex, or Haystack. Build ingestion workflows (OCR, chunking, embedding, semantic search) and integrate with vector databases (FAISS, Pinecone, Qdrant). Ensure seamless integration of GenAI services into business workflows, prioritising security, scalability, and compliance. Collaborate with cross-functional teams (data scientists, architects, engineers More ❯
Experience extending an *AWS stack (Terraform, ECS Fargate, ALB, Secrets Manager, KMS)*. • Hands-on with *LLM APIs* and at least one *vector database* (Pinecone, Weaviate, OpenSearch, etc.). • Multi-tenant data design with GDPR awareness. • CI/CD and automated-testing mindset. Nice-to-Haves • *Solution-architect background*—ability More ❯
others for prompt and model optimisations. Comfortable working with databases (relational & vector), and large-scale data sets and pipelines (e.g. AWS Glue, Redshift, RDS, Pinecone, Opensearch). Hands-on experience with AI Cloud Infrastructure for MLOps (e.g. Google Vertex AI/AWS Bedrock), including deploying AI applications and managing AI More ❯
others for prompt and model optimisations. Comfortable working with databases (relational & vector), and large-scale data sets and pipelines (e.g. AWS Glue, Redshift, RDS, Pinecone, Opensearch). Hands-on experience with AI Cloud Infrastructure for MLOps (e.g.Google Vertex AI/AWS Bedrock), including deploying AI applications and managing AI cloud More ❯
retrieval strategies using vector/graph database technologies. Experience in developing ingestion workflows/pipelines for vector indexes using well-known providers (e.g., FAISS, Pinecone, Weaviate, Chroma). Experience with testing methodologies (unit/functional/e2e) and tools (unittest, pytest, etc.). Experience with agile development methodologies and version … APIs and experience in API design and implementation. Experience in developing ingestion workflows/pipelines for vector indexes using well-known providers (e.g., FAISS, Pinecone, Weaviate, Chroma). Build and deploy robust AI applications using technologies such as Llamanindex and LangChain for seamless orchestration of LLM (large language model) pipelines. More ❯
