LLMOps Engineer: Scale, Secure & Observe GenAI Pipelines

UNAVAILABLE

McLean (VA)

On-site

USD 150,000 - 210,000

Full time

14 days+

Application generator

Stand out for this role — generate a tailored resume and cover letter in about a minute.

Get past ATS filters

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!