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Pinecone Vector Databases Jobs in Washington (NOW HIRING)

Baanyan Software Services, Inc.

Lead AI Engineer

Rockville, MD · On-site

$104K - $137K/yr

LangChain, AWS Strands Vector databases (e.g., PG Vector, Pinecone) Document processing pipelines (OCR, PDF parsing tools) Systems & Infrastructure Cloud platforms (AWS, Google Cloud Platform, or ...

New

Aperio Global

Senior AI/ML Developer with TS/SCI + Polygraph

Mclean, VA · On-site

$116K - $152K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Experience with vector databases such as pgvector , Pinecone , Weaviate , or Chroma to support semantic search and AI/ML workflows * Familiarity with LLM orchestration frameworks such as LangChain ...

Leidos

Cloud Architect / Engineer

Washington, DC · Hybrid

$63.25 - $84.50/hr

Production-ready Retrieval-Augmented Generation (RAG) pipelines, Scalable vector databases (e.g., Pinecone, Weaviate, Milvus, FAISS, or managed services) with indexing, clustering, and optimized ...

Avancer Corporation

Lead AI Engineer

Rockville, MD · On-site

$104K - $137K/yr

Experience with vector databases such as PG Vector or Pinecone. * Background in document processing pipelines, including OCR and PDF parsing. * Experience with cloud platforms such as AWS, Google ...

Trilagen

Agentic AI Engineer

Bethesda, MD · On-site

  • Medical

  • Dental

  • Retirement

  • PTO

... vector databases (Pinecone, pgvector, OpenSearch, Weaviate), semantic search, and hybrid retrieval strategies. You know when to use each and how to keep them performant at scale. Security and ...

Trilagen

Agentic AI Engineer

Bethesda, MD · On-site

  • Medical

  • Dental

  • Retirement

  • PTO

... vector databases (Pinecone, pgvector, OpenSearch, Weaviate), semantic search, and hybrid retrieval strategies. You know when to use each and how to keep them performant at scale. Security and ...

Trilagen

Agentic AI Engineer

Mclean, VA · On-site

  • Medical

  • Dental

  • Retirement

  • PTO

... vector databases (Pinecone, pgvector, OpenSearch, Weaviate), semantic search, and hybrid retrieval strategies. You know when to use each and how to keep them performant at scale. Security and ...

Trilagen

Agentic AI Engineer

Bethesda, MD · On-site

  • Medical

  • Dental

  • Retirement

  • PTO

... vector databases (Pinecone, pgvector, OpenSearch, Weaviate), semantic search, and hybrid retrieval strategies. You know when to use each and how to keep them performant at scale. Security and ...

Trilagen

Agentic AI Engineer

Mclean, VA

  • Medical

  • Dental

  • Retirement

  • PTO

... vector databases (Pinecone, pgvector, OpenSearch, Weaviate), semantic search, and hybrid retrieval strategies. You know when to use each and how to keep them performant at scale. Security and ...

Amtex System Inc.

Lead AI Engineer

Rockville, MD · On-site

$104K - $137K/yr

Experience with vector databases such as PG Vector or Pinecone. * Background in document processing pipelines, including OCR and PDF parsing. * Experience with cloud platforms such as AWS, Google ...

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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

Distinguished AI/ML Engineering Lead

Jobtailor

Washington, DC • On-site

$180 - $230/hr

Other

Posted 11 days ago

Job description

Responsibilities
  • Architect and integrate hybrid AI systems that combine traditional machine learning, deep learning, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines.
  • Design and deploy scalable AI architectures including APIs, microservices, and model-serving frameworks that integrate seamlessly with analytic, simulation, or operational systems.
  • Lead the full AI/ML lifecycle — from data ingestion and feature engineering through training, deployment, and sustainment within secure DoD environments (IL5/IL6, ATO, GovCloud).
  • Engineer event‑driven data pipelines and feature stores for both structured and unstructured data, including text, imagery, and simulation outputs.
  • Ensure Responsible AI practices by embedding traceability, explainability, and confidence scoring into deployed systems.
  • Implement and maintain MLOps pipelines (MLflow, Kubeflow, Airflow, Docker/Kubernetes) to support continuous integration, retraining, and drift detection.
  • Transition R&D prototypes into production, optimizing for mission constraints such as limited compute, edge environments, or disconnected operations.
  • Provide technical leadership and mentorship, setting standards for model quality, architectural design, and ethical AI deployment across programs.
  • Collaborate across engineering, data, and modeling teams to unify FTI’s AI portfolio, ensuring interoperability and reuse across mission systems.
  • Support proposal and solution development, providing technical inputs for AI/ML architectures, data strategies, and Responsible AI assurance frameworks.
Requirements
  • Active Secret clearance required; TS/SCI strongly preferred.
  • Bachelor’s degree in Computer Science, Engineering, or a related technical field (Master’s or Ph.D. preferred).
  • 10+ years of overall experience in AI/ML development, with 5+ years designing and deploying scalable AI/ML architectures, including at least two full lifecycle implementations (from prototype to operational system).
  • Proficiency in Python, PyTorch, TensorFlow, and modern ML frameworks.
  • Experience designing or deploying systems using vector databases (Milvus, Pinecone, Weaviate), knowledge graphs, and semantic search frameworks.
  • Proven ability to design event‑driven data pipelines using Databricks, Spark, Flink, or Kafka.
  • Demonstrated experience deploying AI/ML systems in secure, classified, or edge environments.
  • Familiarity with Responsible AI and assurance principles, including bias detection, explainability, human‑machine teaming, and hallucination prevention.
  • Experience integrating AI models into simulation, modeling, or operational planning systems is highly desirable.
  • Experience transitioning R&D systems into accredited production environments.
  • Strong communication and mentoring skills, with the ability to lead technically while remaining deeply hands‑on.
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