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Qdrant Jobs in Washington (NOW HIRING)

ApplyMint

AI Software Engineer

Herndon, VA · On-site

$125 - $150/hr

Hands‑on experience fine‑tuning, evaluating, and deploying open‑source and proprietary LLMs, embedding models, and vector databases (e.g., Pinecone, Milvus, Qdrant, FAISS). * Programming ...

Dark Wolf Solutions, LLC

AI Software Engineer

Herndon, VA · On-site

$125 - $150/hr

Hands-on experience fine-tuning, evaluating, and deploying open-source and proprietary LLMs, embedding models, and vector databases (e.g., Pinecone, Milvus, Qdrant, FAISS). * Programming & Frameworks:

Dark Wolf Solutions

AI Software Engineer

Chantilly, VA · Remote

$120K - $160K/yr

Hands-on experience fine-tuning, evaluating, and deploying open-source and proprietary LLMs, embedding models, and vector databases (e.g., Pinecone, Milvus, Qdrant, FAISS). * Programming & Frameworks:

Phase2 Technology

AI & ML Engineer

Chantilly, VA · On-site

$125 - $150/hr

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 ...

Phase2 Technology

AI and ML Engineer

Chantilly, VA · On-site

$150 - $200/hr

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 ...

Dark Wolf Solutions

AI Software Engineer

Chantilly, VA · Remote

$120K - $160K/yr

Hands-on experience fine-tuning, evaluating, and deploying open-source and proprietary LLMs, embedding models, and vector databases (e.g., Pinecone, Milvus, Qdrant, FAISS). * Programming & Frameworks:

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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 are popular job titles related to Qdrant jobs in Washington?

For Qdrant jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Qdrant jobs in Washington look for?

The top searched job categories for Qdrant jobs in Washington are:

What cities in Washington are hiring for Qdrant jobs?

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Qdrant jobs near you
Infographic showing various Qdrant job openings in Washington as of August 2026, with employment types broken down into 92% Full Time, 1% Part Time, and 7% Contract. Highlights an 55% Physical, 5% Hybrid, and 40% Remote job distribution.

AI Infrastructure Engineer

Chantilly, VA • On-site

The Josef Group
11 - 50 employees
Learn more about The Josef Groupexternal

$110K - $144K/yr

Full-time

Re-posted 9 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.