76 to 97 of 97 Permanent Chroma Jobs

tool connectivity, enterprise system integration, agent-to-tool communication, and reusable AI workflows. Build and manage vector database solutions using Pinecone, Weaviate, Milvus, FAISS, Chroma, OpenSearch Vector Engine, Azure AI Search, Vertex AI Vector Search, or pgvector. Deploy and optimize LLM/VLLM inference stacks using vLLM, Hugging Face Transformers … architecture , including chunking strategies, embeddings, vector search, hybrid search, reranking, metadata filtering, and evaluation. Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, Chroma, Azure AI Search, OpenSearch, Vertex AI Vector Search, or pgvector. Knowledge of Graph RAG/knowledge graph solutions using Neo4j, Neptune, TigerGraph, RDF, SPARQL, Cypher ...

tool connectivity, enterprise system integration, agent-to-tool communication, and reusable AI workflows. Build and manage vector database solutions using Pinecone, Weaviate, Milvus, FAISS, Chroma, OpenSearch Vector Engine, Azure AI Search, Vertex AI Vector Search, or pgvector. Deploy and optimize LLM/VLLM inference stacks using vLLM, Hugging Face Transformers … architecture , including chunking strategies, embeddings, vector search, hybrid search, reranking, metadata filtering, and evaluation. Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, Chroma, Azure AI Search, OpenSearch, Vertex AI Vector Search, or pgvector. Knowledge of Graph RAG/knowledge graph solutions using Neo4j, Neptune, TigerGraph, RDF, SPARQL, Cypher ...

tool connectivity, enterprise system integration, agent-to-tool communication, and reusable AI workflows. Build and manage vector database solutions using Pinecone, Weaviate, Milvus, FAISS, Chroma, OpenSearch Vector Engine, Azure AI Search, Vertex AI Vector Search, or pgvector. Deploy and optimize LLM/VLLM inference stacks using vLLM, Hugging Face Transformers … architecture , including chunking strategies, embeddings, vector search, hybrid search, reranking, metadata filtering, and evaluation. Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, Chroma, Azure AI Search, OpenSearch, Vertex AI Vector Search, or pgvector. Knowledge of Graph RAG/knowledge graph solutions using Neo4j, Neptune, TigerGraph, RDF, SPARQL, Cypher ...

tool connectivity, enterprise system integration, agent-to-tool communication, and reusable AI workflows. Build and manage vector database solutions using Pinecone, Weaviate, Milvus, FAISS, Chroma, OpenSearch Vector Engine, Azure AI Search, Vertex AI Vector Search, or pgvector. Deploy and optimize LLM/VLLM inference stacks using vLLM, Hugging Face Transformers … architecture , including chunking strategies, embeddings, vector search, hybrid search, reranking, metadata filtering, and evaluation. Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, Chroma, Azure AI Search, OpenSearch, Vertex AI Vector Search, or pgvector. Knowledge of Graph RAG/knowledge graph solutions using Neo4j, Neptune, TigerGraph, RDF, SPARQL, Cypher ...

tool connectivity, enterprise system integration, agent-to-tool communication, and reusable AI workflows. Build and manage vector database solutions using Pinecone, Weaviate, Milvus, FAISS, Chroma, OpenSearch Vector Engine, Azure AI Search, Vertex AI Vector Search, or pgvector. Deploy and optimize LLM/VLLM inference stacks using vLLM, Hugging Face Transformers … architecture , including chunking strategies, embeddings, vector search, hybrid search, reranking, metadata filtering, and evaluation. Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, Chroma, Azure AI Search, OpenSearch, Vertex AI Vector Search, or pgvector. Knowledge of Graph RAG/knowledge graph solutions using Neo4j, Neptune, TigerGraph, RDF, SPARQL, Cypher ...

core AI/ML tooling, including TensorFlow or PyTorch, the Hugging Face ecosystem, and common AI orchestration frameworks. Deep experience with vector databases (Pinecone, ChromaDB, FAISS) and embedding-based retrieval techniques. Advanced understanding of prompt engineering, RAG architecture, model evaluation, and LLM optimization strategies. Practical experience with cloud AI platforms ...

regulated environment. Preferred qualifications:Background in financial services or another regulated enterprise environment. Experience with vector databases (for example pgvector, Pinecone, Weaviate, or Chroma) and agent or orchestration frameworks (for example LangChain, LlamaIndex, or the Model Context Protocol). Experience with MLOps/LLMOps tooling such as experiment tracking, model ...

practices.Experience and Skills (Nice to have)· Previous experience building a user-facing GenAI/LLM software application· Previous experience with vectors and embeddings (pgvector, chromadb)· Knowledge of LLM RAG/Agent core concepts and fundamentals· Experience with Neo4J, GraphQL for efficient data querying for APIs,· CI/CD tools (e.g. ...

regulated environment. Preferred qualifications Background in financial services or another regulated enterprise environment. Experience with vector databases (for example pgvector, Pinecone, Weaviate, or Chroma) and agent or orchestration frameworks (for example LangChain, LlamaIndex, or the Model Context Protocol). Experience with MLOps/LLMOps tooling such as experiment tracking, model ...

consistently across teams.• Retrieval-Augmented Generation (RAG) & Knowledge Architecture: Expertise architecting RAG and knowledge-grounded systems—chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and retrieval evaluation. Familiarity with advanced patterns such as GraphRAG and agentic RAG to maximize factual grounding and minimize hallucination … CrewAI, AutoGen). Experience with cloud AI platforms (e.g., Amazon Bedrock, Azure OpenAI/AI Foundry, Google Vertex AI), vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), containerization and orchestration (Docker, Kubernetes), and distributed computing is advantageous.• Architecture Design: Ability to design end-to-end Generative and Agentic AI architectures ...

business logic to AI models in a standardized, secure way Experience with databases: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Redis), and vector databases (Pinecone, ChromaDB, Weaviate, or Qdrant). Strong understanding of software architecture, design patterns, and API design. Soft skills You lead with ownership and direction. You’re comfortable taking ...

business logic to AI models in a standardized, secure way Experience with databases: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Redis), and vector databases (Pinecone, ChromaDB, Weaviate, or Qdrant). Strong understanding of software architecture, design patterns, and API design. Soft skills You lead with ownership and direction. You’re comfortable taking ...

business logic to AI models in a standardized, secure way Experience with databases: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Redis), and vector databases (Pinecone, ChromaDB, Weaviate, or Qdrant). Strong understanding of software architecture, design patterns, and API design. Soft skills You lead with ownership and direction. You’re comfortable taking ...

business logic to AI models in a standardized, secure way Experience with databases: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Redis), and vector databases (Pinecone, ChromaDB, Weaviate, or Qdrant). Strong understanding of software architecture, design patterns, and API design. Soft skills You lead with ownership and direction. You’re comfortable taking ...

business logic to AI models in a standardized, secure way Experience with databases: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Redis), and vector databases (Pinecone, ChromaDB, Weaviate, or Qdrant). Strong understanding of software architecture, design patterns, and API design. Soft skills You lead with ownership and direction. You’re comfortable taking ...

LANGGRAPH, AND AGENTIC AI FRAMEWORKS/RAG ARCHITECTURE AND ENTERPRISE SEARCH (AZURE AI SEARCH)/OPENAI OR SIMILAR LLM PLATFORMS/VECTOR DATABASES (PGVECTOR, CHROMADB ...

solutions. Desired Qualifications: Experience establishing AI platform capabilities, reference architectures or internal developer frameworks. Experience with vector databases, embeddings, hybrid search, Elasticsearch, Redis, ChromaDB, or similar retrieval technologies. Experience building high-availability banking, capital markets, or other regulated financial applications. Ability to build and communicate complex systems design decisions, trade ...

integration Experience with CPU-based inference, model quantization, and performance tuning Vector Databases & RAG Practical experience with open-source vector databases such as Qdrant, Chroma, Milvus, or pgvector Proven implementation of Retrieval-Augmented Generation (RAG) pipelines Experience generating and managing embeddings and metadata filtering Security & Governance Understanding of data privacy ...

integration Experience with CPU-based inference, model quantization, and performance tuning Vector Databases & RAG Practical experience with open-source vector databases such as Qdrant, Chroma, Milvus, or pgvector Proven implementation of Retrieval-Augmented Generation (RAG) pipelines Experience generating and managing embeddings and metadata filtering Security & Governance Understanding of data privacy ...

just prototypes Demonstrable experience of building and deploying complex AI applications using LLMs and ideally, agentic workflows A familiarity with vector databases (pgvector, ChromaDB, Pinecone or similar) is a plus Benefits include: Flexibility - the team works fully remotely, albeit with frequent meetups to share ideas Stock options Excellent opportunities ...

/React, Python, SQL, OpenAPI.Cloud: AWS (Lambda, API Gateway, S3, SQS, SNS, Aurora, and others as required).Databases: PostgreSQL, Apache Iceberg, DynamoDB, VectorDB (e.g. ChromaDB).IaC and CI/CD: Terraform, AWS CLI, GitLab.AI Development: Agentic AI tooling (e.g. GitHub Copilot, Claude Code).WHAT YOU'LL BE DOING: Strong experience ...

/React, Python, SQL, OpenAPI.Cloud: AWS (Lambda, API Gateway, S3, SQS, SNS, Aurora, and others as required).Databases: PostgreSQL, Apache Iceberg, DynamoDB, VectorDB (e.g. ChromaDB).IaC and CI/CD: Terraform, AWS CLI, GitLab. AI Development: Agentic AI tooling (e.g. GitHub Copilot, Claude Code). WHAT YOU'LL BE DOING ...