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Qdrant Jobs in Washington (NOW HIRING)

Proven experience working with production Vector Databases (e.g., Pinecone, Qdrant, Milvus, or Weaviate) for semantic chunking, embedding generation, and metadata filtering. * Experience deploying ...

Senior AI Security Engineer

Mclean, VA

$115K - $158K/yr

Proven experience working with production Vector Databases (e.g., Pinecone, Qdrant, Milvus, or Weaviate) for semantic chunking, embedding generation, and metadata filtering. * Experience interfacing ...

Proven experience working with production Vector Databases (e.g., Pinecone, Qdrant, Milvus, or Weaviate) for semantic chunking, embedding generation, and metadata filtering. * Experience deploying ...

Senior AI Security Engineer

Mclean, VA · On-site

$115K - $158K/yr

Proven experience working with production Vector Databases (e.g., Pinecone, Qdrant, Milvus, or Weaviate) for semantic chunking, embedding generation, and metadata filtering. * Experience interfacing ...

AI & ML Engineer

Chantilly, VA · On-site

$99K - $225K/yr

Experience with vector databases such as Qdrant and relational databases such as Postgres * Experience with on-prem compute * Knowledge of MITRE ATT&CK framework Clearance: Applicants selected will ...

AI & ML Engineer

Chantilly, VA · On-site +1

$99K - $225K/yr

Experience with vector databases such as Qdrant and relational databases such as Postgres * Experience with on-prem compute * Knowledge of MITRE ATT&CK framework Clearance: Applicants selected will ...

Experience with vector databases such as Qdrant and relational databases such as Postgres * Experience with on-prem compute * Knowledge of MITRE ATT&CK framework Clearance: Applicants selected will ...

AI & ML Engineer

Chantilly, VA · On-site

$99K - $225K/yr

Experience with vector databases such as Qdrant and relational databases such as Postgres * Experience with on-prem compute * Knowledge of MITRE ATT&CK framework Clearance: Applicants selected will ...

Qdrant information

What is the difference between Qdrant vs Data Scientist?

AspectQdrantData Scientist
Required CredentialsTechnical certifications, knowledge of vector databasesDegree in Data Science, Statistics, or related field
Work EnvironmentTech companies, startups, AI-focused firmsResearch labs, tech companies, consulting firms
Industry UsageAI, machine learning, data storageData analysis, predictive modeling, research

Qdrant primarily focuses on managing and deploying vector similarity search databases, requiring technical skills in database management and AI tools. Data Scientists analyze data, build models, and interpret results. While both roles operate within the tech and AI industry, Qdrant specialists are more technical and infrastructure-oriented, whereas Data Scientists focus on data analysis and modeling.

What cities in Washington are hiring for Qdrant jobs? Cities in Washington with the most Qdrant job openings:
Infographic showing various Qdrant job openings in Washington as of July 2026, with employment types broken down into 1% Internship, 85% Full Time, 8% Part Time, and 6% Contract. Highlights an 77% Physical, and 23% Remote job distribution.
AI Infrastructure Engineer

AI Infrastructure Engineer

The Josef Group

Chantilly, VA

$110K - $144K/yr

Full-time

Posted 25 days ago


Job description

AI Infrastructure EngineerTop Secret or TS/SCI is required to start 
$200K to $250K 
Chantilly, VAWhat You'll Do
  • Deploy and optimize self-hosted LLM inference servers (vLLM, Ollama, and similar).
  • Containerize AI workloads using Docker and orchestrate production environments with Kubernetes, including GPU scheduling.
  • Build and maintain AI serving infrastructure, including gateways, load balancing, authentication, TLS, and rate limiting.
  • Optimize GPU utilization, memory management, quantization, batching, and capacity planning to balance performance and cost.
  • Develop and maintain CI/CD pipelines, observability, monitoring, and incident response processes.
What You'll Bring (Required)
  • Hands-on experience deploying and serving Large Language Models (LLMs) in production.
  • Strong experience with Docker and production Kubernetes environments, including GPU scheduling.
  • Deep understanding of self-hosted AI infrastructure, including model formats, quantization, GPU memory management, batching, and inference optimization.
  • Experience supporting production applications with networking, reverse proxies, load balancing, authentication, and TLS.
  • Proficiency with Linux administration and Python and/or Bash scripting.
  • Ownership mindset with the ability to operate and improve production AI infrastructure.
Nice to Have
  • Experience with CUDA, NVIDIA drivers, GPU Operators, or other GPU infrastructure technologies.
  • Experience with Infrastructure as Code (Terraform, Helm).
  • Familiarity with observability and monitoring tools such as Prometheus and Grafana.
  • Experience building Retrieval-Augmented Generation (RAG) pipelines and working with vector databases (pgvector, Qdrant, Weaviate).
  • Experience with LLM gateway tools such as LiteLLM.