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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

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

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:

Virtusa Corporation

Sr. AI/ML Engineer

Atlanta, GA · On-site

$100K - $138K/yr

Fine-tune and deploy LLMs integrated with vector databases (FAISS, Pinecone, ChromaDB). NLP & Deep Learning: Work with transformers, embeddings, multi-modal AI models, and text processing frameworks.

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

  • Medical

  • Retirement

  • PTO

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

RITWIK Infotech Inc

AI Architect

Atlanta, GA · On-site

$60.50 - $79.75/hr

Knowledge of vector databases such as Pinecone, Weaviate, Chroma, or Milvus. * Experience with data engineering platforms such as Databricks, Snowflake, Spark, or Kafka. * Familiarity with DevOps, CI ...

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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

Generative AI Engineer - Alpharetta, GA - F2F

NMK Global Inc.

Alpharetta, GA • On-site

Other

Posted 12 days ago

Job description

Hi Every one,
Hope doing well!.

Job Title: Generative AI Engineer
Location: Alpharetta, GA - F2F
Job Type: Contract
Experience: 7 9 Years

Job Description

We are looking for an experienced Generative AI Engineer to design, develop, and deploy AI-powered applications using Large Language Models (LLMs) and modern AI frameworks. The ideal candidate should have strong expertise in Python, Generative AI, prompt engineering, RAG, vector databases, and cloud platforms. The candidate must be comfortable working in an Agile environment and collaborating with cross-functional teams.

Required Skills
  • 7 9 years of software development experience.
  • Strong experience with Python.
  • Hands-on experience with Generative AI, LLMs, OpenAI, GPT, LangChain, and RAG (Retrieval-Augmented Generation).
  • Experience with Vector Databases (Pinecone, FAISS, ChromaDB, or Milvus).
  • Knowledge of Prompt Engineering and AI model optimization.
  • Experience with REST APIs and microservices.
  • Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.
  • Knowledge of Git, CI/CD, Docker, and Kubernetes.
  • Experience working in Agile/Scrum environments.
  • Excellent communication and problem-solving skills.
Responsibilities
  • Design and build Generative AI solutions using LLMs.
  • Develop AI applications with LangChain and RAG architecture.
  • Integrate AI models with enterprise applications and APIs.
  • Optimize prompts and improve AI model performance.
  • Collaborate with Business Analysts, Developers, and Data Engineers.
  • Deploy and monitor AI applications in cloud environments.
  • Follow best practices for security, scalability, and performance.

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