1 to 25 of 26 Permanent Pinecone Jobs

with Hugging Face and popular LLM training & fine-tuning techniques (LoRA, PEFT, etc.) Experience with Vector/Semantic search, RAG pipelines, or embedding optimization (Pinecone, FAISS, Redis Vector) Familiarity with agent-based frameworks (LangGraph, ReAct, AutoGen, CrewAI, LangChain Agents, etc.). Experience with LLMOps tooling and frameworks (Weights & Biases, MLflow ...

strong exposure to Python and common AI libraries (PyTorch, TensorFlow). Familiarity with: LLMs (OpenAI, Anthropic, Hugging Face, LLaMA models) Vector databases (Pinecone, Weaviate, FAISS , Qudrant) Agentic AI frameworks (exposure to LangGraph, AutoGen, CrewAI or others) Exposure to cloud AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex ...

Face, LangChain) Oversee integration of LLMs (OpenAI, Anthropic, Gemini, Mistral) via enterprise platforms (Azure OpenAI, Vertex AI) Implement RAG architectures and manage vector databases (Pinecone, Weaviate, FAISS, Milvus) to enable enterprise knowledge solutions Data & Engineering Excellence Lead the evolution of modern, enterprise-scale data platforms (Databricks, Snowflake, Synapse, BigQuery) Ensure ...

strategies, tuning methods, and reusable templates. Data Engineering: Handle large-scale unstructured data and integrate AI with enterprise applications via APIs and Vector Databases (Pinecone, PG Vector, Neo4j). Production & MLOps: Establish CI/CD pipelines and manage model deployment using Kubernetes/OpenShift and MLOps principles. Technical Requirements Core ...

cross-functional teams within a fast-paced environment Nice to Have Experience within financial services or other regulated industries Exposure to vector databases (e.g. Pinecone, Weaviate, FAISS) Experience with MLOps/LLMOps and deploying models into production Familiarity with LLM fine-tuning, evaluation, or guardrailing techniques Experience building internal tools ...

Implementing advanced RAG pipelines — hybrid search, re-ranking, query rewriting, context compression, evaluation and grounding. Working with vector stores such as Azure AI Search, Pinecone, Qdrant, Weaviate and Chroma, and tuning embedding models for enterprise data. Building agentic systems using frameworks like LangGraph, Semantic Kernel, LangChain, LlamaIndex and AutoGen — tool ...

Implementing advanced RAG pipelines hybrid search, re-ranking, query rewriting, context compression, evaluation and grounding. Working with vector stores such as Azure AI Search, Pinecone, Qdrant, Weaviate and Chroma, and tuning embedding models for enterprise data. Building agentic systems using frameworks like LangGraph, Semantic Kernel, LangChain, LlamaIndex and AutoGen tool ...

Implementing advanced RAG pipelines hybrid search, re-ranking, query rewriting, context compression, evaluation and grounding. Working with vector stores such as Azure AI Search, Pinecone, Qdrant, Weaviate and Chroma, and tuning embedding models for enterprise data. Building agentic systems using frameworks like LangGraph, Semantic Kernel, LangChain, LlamaIndex and AutoGen tool ...

directly with business and technical stakeholders Preferred Qualifications (Nice to Have) Experience building AI agents or autonomous workflows Familiarity with vector databases such as Pinecone, Weaviate, or FAISS Experience working in startup or fast-paced environments Background in delivering production-grade AI solutions with measurable business impact Exposure to financial ...

Experience with continuous integration and deployment tools such as GitLab, GitHub, or Jenkins. Vector Databases: Experience with and (but not limited to) ChromaDB, Pinecone, PGVector, MongoDB , Qdrant etc. NoSQL: Familiarity with NoSQL databases (e.g., MongoDB preferred). SQL: Experience working with SQL databases like PostgreSQL. Proficient in Git and version ...

Experience with continuous integration and deployment tools such as GitLab, GitHub, or Jenkins. Vector Databases: Experience with and (but not limited to) ChromaDB, Pinecone, PGVector, MongoDB , Qdrant etc. NoSQL: Familiarity with NoSQL databases (e.g., MongoDB preferred). SQL: Experience working with SQL databases like PostgreSQL. Proficient in Git and version ...

environments. Strong expertise in Python and AI/ML libraries: PyTorch, TensorFlow, Scikit‐Learn, Keras, NumPy, Pandas, JAX, XGBoost, PyTorch Geometric, OpenCV, Transformers, LangChain, Pinecone . Proven hands‐on experience deploying models in production and building scalable AI software. Deep understanding of advanced modelling methods: deep learning, GNNs, computer vision ...

orchestration technologies (Docker, Kubernetes) Experience with AI developer tools and productivity accelerators is a plus. Familiarity with vector databases and retrieval patterns; exposure to Pinecone, Qdrant, Aure Search, or similar is beneficial. Strong analytical thinking, problem-solving skills, and attention to engineering quality. Ability to explain technical concepts clearly ...

workflows Designing and maintaining backend services and APIs (primarily Python, FastAPI/Flask) Building and scaling data pipelines and search systems using: ElasticSearch, Weaviate, Pinecone, and other vector databases Engineering secure, containerised architectures using Docker and Kubernetes Ensuring high availability, fault tolerance, and performance for mission-critical workloads Providing technical ...

LangChain, CrewAI) in production. Strong Python engineering skills with solid testing, optimisation, CI/CD, and code quality; Experience implementing and scaling vector databases (Pinecone, Weaviate, Milvus) for retrieval and reasoning workloads. Deep expertise in cloud‐native AI engineering on AWS, with Azure knowledge and ability to design distributed, fault ...

forward deployed roles Strong Python Experience building and deploying LLM applications (OpenAI, Anthropic, Gemini, etc.) Deep familiarity with RAG methodologies, embeddings, and vector databases (Pinecone, Weaviate, pgvector) Robust API integration experience, including working with third-party datasets, web APIs, or scrapers A product mindset, enjoys crafting scrappy prototypes that evolve ...

custom agentic swarms, executing tool-use/function-calling, and implementing the Model Context Protocol (MCP). Context & Data: Deep knowledge of vector databases (Pinecone, CosmosDB Vector, Qdrant), advanced RAG architectures, chunking strategies, and embedding models. Evaluation & Evals: Experience building deterministic guardrails, programmatic evals, and CI/CD pipelines ...

forward deployed roles Strong Python Experience building and deploying LLM applications (OpenAI, Anthropic, Gemini, etc.) Deep familiarity with RAG methodologies, embeddings, and vector databases (Pinecone, Weaviate, pgvector) Robust API integration experience, including working with third-party datasets, web APIs, or scrapers A product mindset, enjoys crafting scrappy prototypes that evolve ...

MongoDB/DynamoDB) Experience with CI/CD, microservices, TDD/BDD. Knowledge of secure system design. Desirable (AI & Data) Vector DBs: Milvus, Weaviate, Pinecone Data orchestration: Airflow, Prefect, Dagster Search: ElasticSearch/OpenSearch AI frameworks: LangChain, Hugging Face MLOps: MLflow, Kubeflow If you are looking for an opportunity ...

MongoDB/DynamoDB) Experience with CI/CD, microservices, TDD/BDD. Knowledge of secure system design. Desirable (AI & Data) Vector DBs: Milvus, Weaviate, Pinecone Data orchestration: Airflow, Prefect, Dagster Search: ElasticSearch/OpenSearch AI frameworks: LangChain, Hugging Face MLOps: MLflow, Kubeflow If you are looking for an opportunity ...

similar. Experience working with Generative AI and ML infrastructure, including: Fine-tuning transformer models Managing embeddings Building RAG pipelines Familiarity with vector databases (e.g., Pinecone, Milvus, Weaviate) and semantic search techniques. Strong foundation in cloud infrastructure, networking, security, CI/CD pipelines, and observability. Experience with cloud platforms ...

execute multi-step tasks. The Data Architect: Proficiency in Vector Databases is essential. You should be comfortable designing data persistence layers using PG Vector, Pinecone, Milvus, or Mongo Atlas to handle large amounts of unstructured data. The MLOps Engineer: You don't just write code; you ship it. Our client ...

execute multi-step tasks. The Data Architect: Proficiency in Vector Databases is essential. You should be comfortable designing data persistence layers using PG Vector, Pinecone, Milvus, or Mongo Atlas to handle large amounts of unstructured data. The MLOps Engineer: You don't just write code; you ship it. Our client ...

queuing (Celery, RabbitMQ, SQS) Event-driven architecture CI/CD pipelines What You'll Work With Python backend services LLM orchestration layers Vector databases (Pinecone, pgvector or similar) API integrations (CRMs, property systems) TypeScript React Next.js Nice to Haves Voice and telephony systems (Twilio, SIP, WebRTC) STT/TTS pipelines ...

field Excellent communication in English, primary language across the business Nice to Have Experience with evaluation frameworks and benchmarking Familiarity with vector databases (e.g. Pinecone, Chroma) Experience with monitoring and observability tools Frontend experience (JavaScript/TypeScript) Experience optimizing systems for latency and cost Exposure to business metrics ...