26 to 50 of 86 Pinecone Jobs

monitoring. 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 More ❯

prompt 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 More ❯

CI/CD : Experience with continuous integration and deployment tools such as GitLab , GitHub , or Jenkins . Database Management 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. Version Control Proficient in Git and version control platforms More ❯

environments (AWS, AZURE, GCP). Operationalization of ML solutions to production. •Experience in Microservices development, API backend development using FastAPI •Relational DB (SQL), Graph DB (Neo4j) and Vector DB (Pinecone, Weviate, Qdrant) •Guide team to debug issues with pipeline failures •Engage with Business/Stakeholders with status update on progress of development and issue fix •Automation, Technology and Process Improvement … MLOps (model/component dockerization, Kubernetes deployment) in multiple environments (AWS, AZURE, GCP). Operationalization of AI solutions to production. •Relational DB (SQL), Graph DB (Neo4j) and Vector DB (Pinecone, Weviate, Qdrant) •Experience designing and implementing ML Systems & pipelines, MLOps practices •Exposure to event driven orchestration, Online Model deployment •Hands on experience in working with client IT/Business teams More ❯

and a good understanding of data consistency trade-offs. Proven knowledge of cloud platforms (e.g., AWS, Azure, or GCP). A Bonus: Experience with graph databases such as Neo4j, Pinecone, or Milvus. Experience building native desktop apps. Experience with NLP libraries and frameworks, such as spaCy or Transformers. Familiarity with machine learning concepts and the ability to work with NLP 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). You are committed to writing clean, maintainable, and scalable code, following best practices in 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). You are committed to writing clean, maintainable, and scalable code, following best practices in More ❯

flows, compliance, user segmentation, etc.) Technical Skills: Proficient in Python, SQL, and data science libraries (Pandas, NumPy, Scikit-learn, Hugging Face Transformers) Familiarity with embedding models, vector databases (e.g., Pinecone, FAISS, Weaviate) Experience with cloud platforms (AWS, GCP, or Azure) and MLOps pipelines Solid understanding of NLP, LLM fine-tuning, and prompt engineering Preferred Qualifications Familiarity with customer analytics and More ❯

day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services on GCP (Vertex AI, Cloud Run, BigQuery) using Terraform and CI/CD best practices … teams to iterate fast and deliver impact Embed security, GDPR compliance, and testing best practices into the core of our stack Tech Stack Python • FastAPI • PostgreSQL + PGVector • Redis • Pinecone/Weaviate • Vertex AI • Cloud Run • Docker • Terraform • GitHub Actions • LangChain/LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background More ❯

day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services on GCP (Vertex AI, Cloud Run, BigQuery) using Terraform and CI/CD best practices … teams to iterate fast and deliver impact Embed security, GDPR compliance, and testing best practices into the core of our stack Tech Stack Python • FastAPI • PostgreSQL + PGVector • Redis • Pinecone/Weaviate • Vertex AI • Cloud Run • Docker • Terraform • GitHub Actions • LangChain/LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background More ❯

day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services on GCP (Vertex AI, Cloud Run, BigQuery) using Terraform and CI/CD best practices … teams to iterate fast and deliver impact Embed security, GDPR compliance, and testing best practices into the core of our stack Tech Stack Python • FastAPI • PostgreSQL + PGVector • Redis • Pinecone/Weaviate • Vertex AI • Cloud Run • Docker • Terraform • GitHub Actions • LangChain/LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background More ❯

optimize RAG pipelines using frameworks such as LangChain, LlamaIndex, or Haystack. Build data ingestion workflows including OCR, chunking, embedding, and semantic search integration. Integrate vector databases such as FAISS, Pinecone, or Qdrant into AI workflows. Deliver scalable GenAI services aligned with security, compliance, and enterprise standards. Collaborate with data scientists, architects, and engineers to implement high-performance AI solutions. Proven More ❯

CI/CD : Experience with continuous integration and deployment tools such as GitLab , GitHub , or Jenkins . Database Management 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. Version Control Proficient in Git and version control platforms More ❯

expertise to support advanced analytical use cases and ML, AI opportunities. Experience with containerisation technologies ( Docker, Kubernetes ) for scalable data solutions. Experience with vector databases and graph databases (e.g., Pinecone, Neo4j, AWS Neptune ). Understanding of data mesh-fabric approaches and modern data architecture patterns . Familiarity with AI/ML workflows and their data requirements. Experience with API specifications More ❯

production Hands-on experience with frameworks like LangChain, LangGraph, or custom-built agent orchestration setups Familiarity with LLM APIs (OpenAI, Anthropic, Mistral, etc.), embedding stores, retrieval pipelines (e.g. Weaviate, Pinecone), and eval tooling Comfort building and testing AI workflows that interact with external APIs, file systems, simulations, and toolchains Bonus: interest or experience in robotics, mechanical/aerospace workflows, or More ❯

. Hands-on experience with LLM orchestration and prompt engineering frameworks such as LangChain or LangGraph, plus designing retrieval-augmented generation (RAG) pipelines. Familiarity with vector databases like Qdrant, Pinecone, or Redis for low-latency AI retrieval. Experience deploying, monitoring, and scaling AI workloads on cloud platforms such as AWS, GCP, or BigQuery. Bonus points for experience with Go , containerization More ❯

up infrastructure using containers, APIs and serverless components Preprocess, chunk and structure unstructured data for optimal use in prompts and context injection Configure and manage vector databases such as Pinecone or Azure AI Search Implement retrieval-augmented generation flows using vector databases and embedding models Develop orchestration logic for multi-step agents and tool-using language models Monitor system performance More ❯

production Hands-on experience with frameworks like LangChain, LangGraph, or custom-built agent orchestration setups Familiarity with LLM APIs (OpenAI, Anthropic, Mistral, etc.), embedding stores, retrieval pipelines (e.g. Weaviate, Pinecone), and eval tooling Comfort building and testing AI workflows that interact with external APIs, file systems, simulations, and toolchains Bonus: interest or experience in robotics, mechanical/aerospace workflows, or More ❯

production Hands-on experience with frameworks like LangChain, LangGraph, or custom-built agent orchestration setups Familiarity with LLM APIs (OpenAI, Anthropic, Mistral, etc.), embedding stores, retrieval pipelines (e.g. Weaviate, Pinecone), and eval tooling Comfort building and testing AI workflows that interact with external APIs, file systems, simulations, and toolchains Bonus: interest or experience in robotics, mechanical/aerospace workflows, or More ❯

production Hands-on experience with frameworks like LangChain, LangGraph, or custom-built agent orchestration setups Familiarity with LLM APIs (OpenAI, Anthropic, Mistral, etc.), embedding stores, retrieval pipelines (e.g. Weaviate, Pinecone), and eval tooling Comfort building and testing AI workflows that interact with external APIs, file systems, simulations, and toolchains Bonus: interest or experience in robotics, mechanical/aerospace workflows, or More ❯

large-scale infrastructure, and modern backend development using Java, Python, Golang, Spring Boot, Flask, and Kubernetes. We focus on integrating RAG-powered LLMs, implementing advanced vector search (FAISS, Milvus, Pinecone), and building scalable and high-performance AI-driven solutions. You Might Be a Good Fit If You: Have deep hands-on software engineering expertise in Java or Python Thrive in … applications using Java, Python, and modern backend frameworks Integrate LLMs into enterprise-scale systems using internal frameworks and libraries Design and implement vector search solutions using FAISS, Milvus, and Pinecone Build scalable APIs and backend services using Spring Boot, Flask, and FastAPI Optimize data storage and retrieval with PostgreSQL/MongoDB and distributed databases Deploy and manage cloud-native applications … Succeed in This Role: Proficiency in Java or Python for backend development Strong knowledge of Spring Boot, Flask, FastAPI, and API design Experience with vector search frameworks (FAISS, Milvus, Pinecone) Expertise in Kubernetes and Docker for scalable deployment Understanding of authentication & security frameworks (Spring Security, SSO) Hands-on experience with PostgreSQL and distributed storage Experience with Maven or Gradle for More ❯

Sub, and Vertex AI. Support AI engineers by managing structured and unstructured data ingestion, embedding pipelines, and vector database integrations. Implement retrieval-augmented generation (RAG) systems using tools like Pinecone, FAISS, Chroma, or PostgreSQL. Develop infrastructure to support short- and long-term memory in autonomous agents. Work with AI orchestration frameworks (LangChain, LangGraph, CrewAI) to ensure reliable data integration and … strong data governance, access control, and compliance practices. Tech Stack: Languages: Python, SQL Cloud: Google Cloud Platform (BigQuery, Dataflow, Vertex AI, Cloud Run, Pub/Sub) Databases: PostgreSQL, BigQuery, Pinecone, FAISS, Chroma Tools: dbt, Airflow, Terraform, Docker, GitHub Actions AI Frameworks: LangChain, LangGraph, LangFlow, CrewAI, OpenAI APIs What We’re Looking For: Strong experience building and maintaining data systems on More ❯

Sub, and Vertex AI. Support AI engineers by managing structured and unstructured data ingestion, embedding pipelines, and vector database integrations. Implement retrieval-augmented generation (RAG) systems using tools like Pinecone, FAISS, Chroma, or PostgreSQL. Develop infrastructure to support short- and long-term memory in autonomous agents. Work with AI orchestration frameworks (LangChain, LangGraph, CrewAI) to ensure reliable data integration and … strong data governance, access control, and compliance practices. Tech Stack: Languages: Python, SQL Cloud: Google Cloud Platform (BigQuery, Dataflow, Vertex AI, Cloud Run, Pub/Sub) Databases: PostgreSQL, BigQuery, Pinecone, FAISS, Chroma Tools: dbt, Airflow, Terraform, Docker, GitHub Actions AI Frameworks: LangChain, LangGraph, LangFlow, CrewAI, OpenAI APIs What We’re Looking For: Strong experience building and maintaining data systems on More ❯

building and testing new functionality, troubleshooting customer issues, finding root causes, and developing improvements to ensure maximal user impact and performance. Our RAG system is based on Python and Pinecone and we have deployed a set of open source models. We interact with our code and data traceability graph through our main application stack which is currently based on Next.js … with and foundational understanding of LLMs (especially open source models), including production deployment Experience with and foundational understanding of non-LLM AI, including production deployment Experience with RAG systems (Pinecone or similar) Strong interest in programming languages, parsing algorithms, interpreters, and compilers Extensive experience in Python, Pandas, and at least one other programming language Experience with and clear understanding of More ❯

building and testing new functionality, troubleshooting customer issues, finding root causes, and developing improvements to ensure maximal user impact and performance. Our RAG system is based on Python and Pinecone and we have deployed a set of open-source models. We interact with our code and data traceability graph through our main application stack which is currently based on Next.js … with and foundational understanding of LLMs (especially open source models), including production deployment Experience with and foundational understanding of non-LLM AI, including production deployment Experience with RAG systems (Pinecone or similar) Strong interest in programming languages, parsing algorithms, interpreters, and compilers Extensive experience in Python, Pandas, and at least one other programming language Experience with and clear understanding of More ❯