Staff MLOps Engineer (LLMOps)

TL;DR

Staff MLOps Engineer (LLMOps): Building and scaling the technical infrastructure for AI/ML systems with an accent on LLM pipelines and agentic systems. Focus on automating model versioning, optimizing model performance, and deploying scalable serving infrastructure to enable rapid AI innovation.

Location: Remote (North America / US-based)

Salary: $200,000 - $275,000 (US base salary)

Company

hirify.global provides AI-powered intelligence solutions to help public and private sector agencies investigate and disrupt crime using blockchain data.

What you will do

• Build reusable CI/CD workflows for model training, evaluation, and deployment using GitHub Actions and Langfuse.
• Develop a modular AI infrastructure stack including vector databases, feature stores, and model registries.
• Deploy and maintain LLM and agentic workflows in production, focusing on cost, latency, and performance optimization.
• Partner with data science teams to embed AI models into real-time applications and workflows.
• Ensure data accuracy, consistency, and reliability for optimized model training and inferencing.
• Implement infrastructure for offline and online evaluation of LLMs, including regression testing and human-in-the-loop workflows.

Requirements

• Must be based in North America (US base salary focus).
• Proficiency in writing high-quality, maintainable software, primarily in Python.
• Strong background in scalable infrastructure: Docker, Kubernetes, and Terraform.
• Experience with ML Ops best practices, including model versioning and automated drift detection.
• Ability to deploy scalable model serving infrastructure (e.g., vLLM, Triton, BentoML).
• Experience capturing traces and monitoring performance for LLM workflows in production.

Nice to have

• Experience with LangChain, LlamaIndex, MLflow, or BentoML.

Culture & Benefits

• High-velocity, high-ownership environment focused on rapid issue resolution and impact.
• Mission-driven work at the intersection of AI, national security, and fighting crime.
• Distributed-first organizational structure with multiple global hubs.
• Eligibility to participate in the company equity plan.
• Strong emphasis on AI fluency as a baseline expectation for all employees.