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Pinecone Vector Databases Jobs (NOW HIRING)

Manage and optimize vector databases (e.g., Pinecone, Weaviate, Milvus) * Design and optimize Retrieval-Augmented Generation (RAG) pipelines for performance and scalability * Implement AI governance ...

... vector databases Pinecone or Chroma or FAISS Ability to quickly conduct experiments and analyze the features and capabilities of newer versions of the LLM models as they come into market Basic data ...

Manage and optimize vector databases (e.g., Pinecone, Weaviate, Milvus) * Design and optimize Retrieval-Augmented Generation (RAG) pipelines for performance and scalability * Implement AI governance ...

Lead AI/ML Engineer

Plano, TX · On-site

$98K - $129K/yr

Vector Databases & Retrieval Pipelines: Implement andmaintainvector stores (OpenSearch, Pinecone, Milvus,Qdrant) and design efficient similarity search, retrieval workflows, and indexing strategies.

... vector databases Pinecone or Chroma or FAISS Ability to quickly conduct experiments and analyze the features and capabilities of newer versions of the LLM models as they come into market Basic data ...

... vector databases Pinecone or Chroma or FAISS Ability to quickly conduct experiments and analyze the features and capabilities of newer versions of the LLM models as they come into market Basic data ...

Experience with vector databases (FAISS/Milvus/Pinecone/pgvector) and document processing (PDF/HTML/markdown, chunking strategies). * Solid understanding of API security (OAuth2/OIDC/JWT), networking ...

Developer Advocate

San Francisco, CA · On-site

$140K - $240K/yr

Its leading vector database and knowledge engine, Pinecone Nexus, power accurate, performant AI applications for more than 9,000 customers and 800,000 developers worldwide. Pinecone's mission is to ...

Work with vector databases (FAISS, Pinecone, Chroma, Weaviate) for semantic search. * Monitor, evaluate, and optimize GenAI models for accuracy, performance, and cost. Expertise You'll Bring: * 5+ ...

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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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AWS AI Engineer

AWS AI Engineer

Tekwissen

Herndon, VA • On-site

Other

Posted 8 days ago


Job description

Role: AWS AI Engineer
Location: Herndon, VA
Work Mode: Onsite
About the Role
We are seeking a highly skilled AWS AI Engineer with strong hands-on experience in Kubernetes, EKS, and Generative AI systems. The ideal candidate will have deep expertise in deploying, scaling, and maintaining AI/ML workloads in production environments, along with experience in modern AI frameworks and platform engineering.
Certification Requirements (At least one required)
  • Active AWS Solutions Architect Certification
  • AWS Certified AI Foundations or AWS Certified AI Professional
  • Certified Kubernetes Administrator (CKA) - Active certification
Key Responsibilities
  • Deploy, manage, and troubleshoot Kubernetes clusters, including disconnected installations
  • Design, deploy, and upgrade Amazon EKS clusters in production environments
  • Perform advanced troubleshooting for EKS and Kubernetes-based systems
  • Implement and manage LLMOps workflows, including deployment, monitoring, and scaling of Generative AI systems
  • Build and maintain agent-based workflows using frameworks like LangChain, CrewAI, or AutoGen
  • Manage and optimize vector databases (e.g., Pinecone, Weaviate, Milvus)
  • Design and optimize Retrieval-Augmented Generation (RAG) pipelines for performance and scalability
  • Implement AI governance frameworks, including security guardrails and cost optimization strategies
  • Build and support Internal Developer Platforms (IDP) for AI use cases
Must-Have Skills
  • Strong Kubernetes expertise (installation, administration, troubleshooting)
  • Extensive hands-on experience with Amazon EKS (deployment, upgrades, troubleshooting)
  • Proven experience with LLMOps and production-grade Generative AI systems
  • Experience with agentic AI frameworks (LangChain, CrewAI, AutoGen)
  • Hands-on experience with vector databases and RAG architectures
  • Knowledge of AI governance, security guardrails (e.g., NeMo Guardrails), and cost control for LLMs
  • Experience building AI-focused Internal Developer Platforms

TekWissen logo

About TekWissen

Sourced by ZipRecruiter

TekWissen is an emerging global human capital, recruitment and IT services organization. Operating since 2009, we draw upon more than a decade of staffing experience to deliver critical talent acquisition solutions and IT engagements for our clients. We’re founded on a culture that is passionate about delivering tailored solutions, that create lasting partnerships.

Industry

Recruiting and staffing services

Company size

501 - 1,000 Employees

Headquarters location

Ann Arbor, MI, US

Year founded

2009

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