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Pinecone Vector Databases Jobs in Suwanee, GA (NOW HIRING)

AGS

DevOps Platform Engineer

Duluth, GA · On-site

$48.50 - $66.50/hr

Provision and manage the agentic AI platform infrastructure - LLM API gateway, vector database (Pinecone/pgvector), container-based agent deployment, and model serving endpoints * Container ...

Collabera

AI/ML Engineer

Atlanta, GA · On-site

$50 - $70/hr

Knowledge of vector databases such as Pinecone, Chroma, FAISS, Weaviate, or similar is a plus. Experience deploying AI applications into production is highly preferred. What the client is looking for:

SDH Systems

Senior AI Full Stack Engineer

Norcross, GA · On-site

Deep expertise in RAG architecture: document processing, embedding models, chunking strategies, semantic search, vector databases (Pinecone, Weaviate, Chroma, pgvector, Qdrant). * Experience with ...

New

Sumeru

Senior AI/ML Engineer

Atlanta, GA · On-site

$100K - $138K/yr

Support integration with vector databases (e.g., Pinecone, pgvector, Qdrant) for semantic search across customer data Education and Work Experience * Bachelor's or Master's degree in Computer Science ...

Waystar, Inc

Senior ML Engineer

Atlanta, GA · On-site

$100 - $160/hr

Experience with vector databases (e.g., pgvector, Pinecone, Weaviate, Qdrant) and retrieval‑augmented generation architectures. * Exposure to the healthcare domain and familiarity with medical ...

PlayAGS, Inc.

DevOps Platform Engineer

Duluth, GA · On-site

$120 - $180/hr

Provision and manage the agentic AI platform infrastructure -- LLM API gateway, vector database (Pinecone/pgvector), container-based agent deployment, and model serving endpoints * Container ...

New

Finastra

Senior AI Engineer

Atlanta, GA

$100K - $138K/yr

Vector Databases: Familiarity with vector stores (Pinecone, Weaviate, ChromaDB, FAISS, etc.) for embeddings and retrieval * Software Development: Understanding of Git workflows, version control with ...

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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.

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Pinecone Vector Databases jobs near you

Agentic AI Engineer (LLM Orchestration & Multi-Agent Systems) - Q3-2026

R2 Technologies Corporation

Alpharetta, GA • On-site

Full-time

Posted 10 days ago

Job description

Overview:
About R2 Technologies: R2 Technologies is a Certified Minority Business Enterprise (MBE) headquartered in Alpharetta, GA. With over two decades of experience across global markets, we provide IT staffing and digital product engineering services to clients ranging from startups to Fortune 1000 companies. In addition to talent services, R2 develops proprietary solutions including SmartEnt, an enterprise AI and IoT intelligence platform. We work closely with our clients to deliver technology outcomes that are realistic, measurable, and impactful.
Job Summary: The chatbot era is over-enterprises now want software that acts, not just answers. R2 Technologies is seeking an Agentic AI Engineer to design and ship production AI agents that plan, reason, call tools, and complete multi-step business workflows end to end. You will work across the full lifecycle of an agent-orchestration design, tool integration, memory and context management, evaluation, and production deployment-building systems that power enterprise client applications and our own SmartEnt platform.
Key Responsibilities:
  • Design and build production AI agents and multi-agent orchestration workflows using LangGraph, LangChain, CrewAI, or the Claude Agent SDK.
  • Implement agent reasoning, planning, tool/function calling, and memory and state management under real token, latency, and cost constraints.
  • Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models to ground agents in enterprise data.
  • Integrate Large Language Models-Anthropic Claude, OpenAI GPT, Google Gemini-into enterprise applications via AWS Bedrock, Azure OpenAI, or direct API and MCP-based tool integration.
  • Develop programmatic agent evaluations, including offline and online metrics, trajectory scoring, and failure-mode analysis to move agents from demo to dependable.
  • Implement guardrails, tracing, and observability to ensure agent decisions are auditable and compliant with enterprise security and governance standards.

Qualifications:
  • 3 years of experience in AI/ML Engineering, Backend Engineering, or applied LLM development.
  • Strong hands-on proficiency in Python, with working knowledge of Java, TypeScript, or Go.
  • Hands-on experience with agentic frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Google ADK, or the Claude Agent SDK.
  • Proven experience building RAG architectures with vector databases (Pinecone, Weaviate, ChromaDB, FAISS, or Milvus).
  • Strong understanding of prompt engineering, context engineering, embeddings, and model evaluation.
  • Experience deploying containerized services on AWS, Azure, or GCP using Docker, Kubernetes, and CI/CD pipelines.

Skills:
Agentic AI, LangGraph, LangChain, Python, RAG, MCP, Claude, OpenAI, Vector Databases, AWS Bedrock, Multi-Agent Systems
Skills:
Agentic AI,LangGraph,LangChain,Python,RAG,Multi-Agent Systems,LLM Integration,Vector Databases,Prompt Engineering

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