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LLMOps Engineer

Job in McLean, Fairfax County, Virginia, USA
Listing for: TechWish
Full Time position
Listed on 2026-07-01
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Engineering
Job Description & How to Apply Below

Senior LLMOps Engineer

FTE Grade: 7

Location:

REMOTE

Note:

This Position Is Not Eligible For Immigration Sponsorship At This Time.

Primary Responsibilities:

  • Design, develop, and deploy AI solutions leveraging cutting-edge tools, frameworks, and best practices.
  • Fine-tune and deploy Large Language Models (LLMs) such as OpenAI and Azure-based services to deliver scalable AI applications.
  • Implement and optimize Retrieval-Augmented Generation (RAG) architectures, leveraging vector databases for efficient data retrieval.
  • Write clean, maintainable Python code following software development best practices, including GIT workflows, code reviews, and CI/CD pipelines.
  • Collaborate with cross-functional teams to integrate AI solutions with existing data engineering pipelines and cloud infrastructure.
  • Build and manage containerized AI applications using Docker and Kubernetes for scalability and reproducibility.
  • Leverage Azure services for deploying, monitoring, and scaling AI applications in the cloud.

Required Qualifications:

  • Strong proficiency in Python programming with a deep understanding of the software development lifecycle.
  • Expertise in working with LLMs, including fine-tuning, prompt engineering, and deployment.
  • Hands-on experience with RAG architectures and vector databases for knowledge retrieval.
  • Familiarity with SQL Server and/or Snowflake for data storage and retrieval.
  • Proficiency in Docker for containerization and Kubernetes for orchestration of containerized applications.
  • Solid understanding of Azure services for deploying and managing AI applications.

Preferred Qualifications:

  • Experience with advanced optimization techniques for LLMs in production.
  • Exposure to managing and deploying large-scale AI systems in cloud environments.
  • Knowledge of best practices in logging, monitoring, and debugging AI workflows.
  • Ability to collaborate effectively with data engineering teams for seamless integration of AI pipelines.
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