LLMOps Engineer

UNAVAILABLE; McLean (VA); On-site; USD 150,000 - 210,000; Full time

Job summary

We are seeking an LLMOps Engineer to design, build, operate, and optimize the pipelines and infrastructure powering our predictive and generative AI capabilities. You will collaborate with AI Product Engineers, Data Scientists, and senior LLMOps staff to deploy, monitor, and continuously improve LLM-based systems, with opportunities to advance to senior roles.

The role is highly technical and hands-on, focusing on production readiness, system ownership, and architectural contributions in a

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field.
• 5+ years of experience; OR Master’s degree with 3+ years of experience.
• 2+ years of experience in software engineering, DevOps, MLOps, cloud engineering, or data engineering with exposure to LLM/ML model operations.
• Proficiency in Python and familiarity with LLM-related tools and frameworks such as Hugging Face Transformers, LangChain, LlamaIndex, or similar.
• Experience with containerization (Docker) and basic orchestration using Kubernetes or serverless hosting environments.
• Hands-on knowledge of cloud platforms (AWS, Azure, or GCP) with compute, storage, and networking for AI workloads.
• Familiarity with CI/CD pipelines, automated testing, and environment provisioning for AI or data systems.
• Exposure to vector databases, embedding models, and RAG pattern implementations preferred.
• Understanding of modern DevSecOps principles, security basics, and safe handling of data used in AI pipelines.
• Strong debugging and problem-solving skills with the ability to collaborate in cross-functional engineering teams.
• Strong written and verbal communication skills with the ability to document workflows and explain operational concepts.
• Experience working in agile or iterative development environments is a plus.

Responsibilities

• Implement and maintain core LLM pipelines, including model hosting endpoints, embedding pipelines, retrieval layers, and vector database integrations.
• Build automation for model versioning, dataset management, and configuration tracking to support reproducible and reliable GenAI development.
• Develop monitoring and observability components for LLM-based applications, including metrics dashboards, alerting, and logging of model outputs and performance.
• Collaborate with AI Product Engineers to move prototypes into production environments, supporting scalability, testing, and integration with mission systems.
• Assist in configuring and optimizing inference runtimes, containers, and microservices to ensure responsive and cost-efficient model operations.
• Contribute to CI/CD pipelines that support automated testing, evaluation workflows, and safe deployment of updated prompts, models, and context strategies.
• Help implement and maintain basic MLSecOps guardrails, such as input validation, prompt protections, and output filtering.
• Participate in troubleshooting efforts, root-cause analysis, and issue resolution for operational incidents involving LLM pipelines or infrastructure.
• Stay current with emerging tools, libraries, and practices for operationalizing LLMs, including orchestration frameworks and inference accelerators.
• Document system workflows, runbooks, and operational best practices to support team knowledge growth and onboarding.
• You will contribute to the growth of our AI & Data Exploitation Practice!

Skills

Python

DevOps

MLOps

Public trust

Education

Bachelor’s or Master’s degree in CS/Data/ML or related field

Master’s degree in CS/Data/ML or related field

Tools

Hugging Face Transformers

LangChain

LlamaIndex

Docker

Kubernetes

AWS

Azure

GCP

Job description

Overview

We are looking for a skilledLLMOpsEngineerto help build,operate, andoptimizethe infrastructure and pipelines that power our predictive and generative AI capabilities. TheLLMOpsEngineer will work closely with AI Product Engineers, Data Scientists, and seniorLLMOpsstaff to support the deployment, monitoring, and continuous improvement of LLM-based systems. This role is highly technical and hands-on, with opportunities to grow into senior roles through increasing independence, system ownership, and architectural contributions.

Responsibilities

• Implement andmaintaincore LLM pipelines, including model hosting endpoints, embedding pipelines, retrieval layers, and vector database integrations.
• Build automation for model versioning, dataset management, and configuration tracking to support reproducible and reliable GenAI development.
• Develop monitoring and observability components for LLM-based applications, includingmetricsdashboards, alerting, and logging of model outputs and performance.
• Collaborate with AI Product Engineers to move prototypes into production environments, supporting scalability, testing, and integration with mission systems.
• Assistin configuring andoptimizinginference runtimes, containers, and microservices to ensure responsive and cost-efficient model operations.
• Contribute to CI/CD pipelines that support automated testing,evaluationworkflows, and safe deployment of updated prompts, models, and context strategies.
• Help implement andmaintainbasicMLSecOpsguardrails, such as input validation, prompt protections, and output filtering.
• Participate in troubleshooting efforts, root-cause analysis, and issue resolution for operational incidents involving LLM pipelines or infrastructure.
• Stay current with emerging tools, libraries, and practices for operationalizing LLMs, including orchestration frameworks and inference accelerators.
• Document system workflows, runbooks, and operational best practices to support teamknowledgegrowth and onboarding.
• You will contribute to the growth of our AI & Data Exploitation Practice!

Qualifications

• Ability to hold aposition of public trustwith the U.S. government.
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field and 5+ years of experience; OR
  • Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field and 3+ years of experience.
• 2+ yearsof experience in software engineering, DevOps,MLOps, cloud engineering, or data engineering, with exposure to LLM or ML model operations.
• Proficiencyin Python and familiarity with LLM-related tools and frameworks such asHugging Face Transformers,LangChain,LlamaIndex, or similar.
• Experience withcontainerization (Docker)and basic orchestration usingKubernetesor serverless model hosting environments.
• Hands-on knowledge of cloud platforms (AWS, Azure, or GCP), includingcompute, storage, and networking fundamentals for AI workloads.
• Familiarity withCI/CD pipelines, automated testing, and environment provisioning for AI or data systems.
• Exposure tovector databases, embedding models,and RAG pattern implementations preferred.
• Understanding ofmodernDevSecOpsprinciples, security basics, and safe handling of data used in AI pipelines.
• Strong debugging and problem-solving skills withanability to collaborate in cross-functional engineering teams.
• Strong written and verbal communication skills with the ability to document workflows and explain operational concepts.
• Experience working in agile or iterative development environments is a plus.