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Pinecone Vector Databases Jobs in Riverside, CA (NOW HIRING)

Ingrammicro

Software Engineer - AI

Irvine, CA

PostgreSQL, SQL databases, NoSQL databases, Vector Databases (pgvector, Pinecone, Weaviate, ChromaDB, etc.) * Exposure to one or more Cloud Platforms, such as: Google Cloud Platform (GCP), Amazon Web ...

Ingram Micro

Software Engineer - AI

Irvine, CA · On-site

PostgreSQL, SQL databases, NoSQL databases, Vector Databases (pgvector, Pinecone, Weaviate, ChromaDB, etc.) * Exposure to one or more Cloud Platforms, such as: Google Cloud Platform (GCP), Amazon Web ...

CLPS Global

AI Engineer (Mississauga, ON- Canada)

Ontario, CA · On-site

$116K - $139K/yr

... various vector databases (e.g., PG Vector, Pinecone, Mongo Atlas, Neo4j) for efficient data storage and retrieval. o Experience in dealing with large amounts of unstructured data and designing ...

FieldAI

Senior Machine Learning Platform Engineer

Irvine, CA · On-site

$112K - $154K/yr

Experience with vector databases (OpenSearch, Pinecone, Weaviate) for indexing and retrieval. * Familiarity with distributed training frameworks (Horovod, DDP/FSDP, DeepSpeed, Ray). * Hands-on ...

All JobsPinecone Vector Databases JobsPinecone Vector Databases Jobs in Riverside, CA

Pinecone Vector Databases information

What is a Pinecone Vector Database?

A Pinecone Vector Database is a cloud-based service designed to efficiently store, index, and search high-dimensional vector data, such as embeddings generated by machine learning models. It enables fast similarity search, making it ideal for use cases like semantic search, recommendation systems, and AI-powered applications. Pinecone handles the complexity of scaling and managing vector data, so developers can focus on building intelligent applications without worrying about infrastructure.

What are the key skills and qualifications needed to thrive as a Pinecone Vector Database Engineer, and why are they important?

To thrive as a Pinecone Vector Database Engineer, you need a strong background in computer science, data engineering, and experience with large-scale distributed systems, often supported by a relevant degree or equivalent experience. Proficiency in Python, REST APIs, cloud platforms (AWS, GCP), and vector search technologies, along with familiarity with Pinecone’s SDK and database management, are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you collaborate with cross-functional teams and deliver scalable solutions. These skills ensure robust database performance, efficient data retrieval, and successful integration of vector search capabilities into real-world applications.

What are some common challenges faced by engineers working with Pinecone Vector Databases, and how can they be addressed?

Engineers working with Pinecone Vector Databases often encounter challenges such as optimizing vector search performance at scale, ensuring data consistency across distributed systems, and integrating the database with various machine learning pipelines. Addressing these challenges typically involves tuning indexing parameters, monitoring resource utilization, and collaborating closely with data scientists to understand retrieval requirements. Regularly reviewing documentation and participating in community forums can also help engineers stay current with best practices and new features.

What is the difference between Pinecone Vector Databases vs Data Engineers?

AspectPinecone Vector DatabasesData Engineers
Primary RoleManaging and deploying vector database solutions for AI/ML applicationsDesigning, building, and maintaining data pipelines and infrastructure
Skills & CertificationsKnowledge of vector databases, cloud platforms, programming (Python, SQL)Data modeling, ETL processes, cloud services, programming (Python, Java)
Work EnvironmentTech companies, AI startups, cloud providersData-driven organizations, tech firms, finance, healthcare

While Pinecone Vector Databases specialists focus on deploying and managing vector database solutions for AI applications, Data Engineers build and maintain the data infrastructure that supports these systems. Both roles require programming skills and familiarity with cloud platforms, but their core responsibilities differ: one centers on database management, the other on data pipeline development.

What are popular job titles related to Pinecone Vector Databases jobs in Riverside, CA? For Pinecone Vector Databases jobs in Riverside, CA, the most frequently searched job titles are:
What job categories do people searching Pinecone Vector Databases jobs in Riverside, CA look for? The top searched job categories for Pinecone Vector Databases jobs in Riverside, CA are:
What cities near Riverside, CA are hiring for Pinecone Vector Databases jobs? Cities near Riverside, CA with the most Pinecone Vector Databases job openings:
Pinecone Vector Databases jobs near you

Senior Data Engineer GenAI / RAG / LangChain / LangGraph

Ravh IT Solutions

Irvine, CA • On-site

$113K - $154K/yr

Other

Posted 5 days ago

Job description

Job Title: Senior Data Engineer – GenAI / RAG / LangChain / LangGraph

Location: Irvine
Experience: 10–15+ Years
Employment Type: Contract

Job Description

We are seeking a highly experienced Senior Data Engineer with expertise in modern data engineering and Generative AI technologies. The ideal candidate should have hands-on experience designing scalable data platforms while building AI-powered applications using RAG (Retrieval-Augmented Generation), LangChain, LangGraph, LLMs, and Vector Databases.

The candidate should possess strong cloud data engineering expertise along with practical experience integrating Large Language Models into enterprise applications.

Mandatory Skills

 

Data Engineering

  • 8+ years of experience in Data Engineering
  • Strong expertise in Python and SQL
  • Apache Spark / PySpark
  • Databricks
  • ETL/ELT Pipeline Development
  • Delta Lake
  • Data Warehousing & Data Lake Architecture
  • Apache Airflow or equivalent orchestration tools
  • CI/CD for Data Pipelines
  • Git / Azure DevOps / GitHub

Cloud Platforms (Any One)

  • Microsoft Azure (ADF, Synapse, ADLS)
  • AWS (Glue, EMR, Lambda, S3, Athena)
  • Google Cloud Platform (BigQuery, Dataflow)

Generative AI / LLM

  • Hands-on experience building RAG (Retrieval-Augmented Generation) solutions
  • LangChain
  • LangGraph
  • OpenAI / Azure OpenAI / Anthropic Claude / Gemini APIs
  • Prompt Engineering
  • AI Agents / Multi-Agent Workflows
  • LLM Orchestration
  • Function Calling / Tool Calling
  • LLM Evaluation and Optimization

Vector Databases

Experience with one or more:

  • Pinecone
  • ChromaDB
  • FAISS
  • Weaviate
  • Milvus
  • Azure AI Search

AI/ML

  • Machine Learning fundamentals
  • Embedding Models
  • Semantic Search
  • Document Processing
  • NLP
  • Model Deployment (preferred)

Additional Skills

  • REST APIs / FastAPI
  • Docker
  • Kubernetes (Preferred)
  • MLflow
  • Kafka (Preferred)

Responsibilities

  • Design and develop scalable enterprise data pipelines.
  • Build Retrieval-Augmented Generation (RAG) applications.
  • Develop AI Agents using LangChain and LangGraph.
  • Integrate enterprise data sources with LLMs.
  • Build semantic search solutions using vector databases.
  • Optimize prompt engineering and LLM performance.
  • Work with structured and unstructured data sources.
  • Collaborate with Data Scientists, ML Engineers, and Business stakeholders.
  • Ensure data quality, governance, scalability, and security.

Preferred Experience

  • Financial Services / Asset Management
  • Banking
  • Healthcare
  • Insurance
  • Retail
  • Manufacturing

Nice to Have

  • Microsoft Fabric
  • Snowflake
  • DBT
  • MLOps
  • Hugging Face
  • LlamaIndex
  • CrewAI / AutoGen
  • MCP (Model Context Protocol)
  • Knowledge Graphs
  • GraphRAG

Recruiter Screening Checklist

Candidates must have:

  •  8+ years of Data Engineering experience
  •  Strong Python & SQL
  •  Databricks / Spark
  •  Azure or AWS
  •  RAG implementation experience
  •  LangChain
  •  LangGraph
  •  OpenAI / Azure OpenAI
  •  Vector Database experience
  •  AI Agent development
  •  Production deployment of LLM applications
  •  Strong communication skills

Apply