LLM Ops Engineer

We are hiring an LLM Ops Engineer to join our AI Research team a highly technical group working on cutting-edge advancements in the AI industry. The team focuses on building scalable production-grade LLM systems fine-tuning strategies evaluation frameworks and next-generation deployment architectures.

This role requires hands-on experience operating LLMs beyond simple API integration. The ideal candidate understands the architectural operational and evaluation complexities that differentiate LLMOps from traditional MLOps.

Responsibilities:- 

• Manage the end-to-end lifecycle of LLMs: registry packaging versioning deployment monitoring and rollback.
• Deploy and operate self-hosted / open-source LLMs (not limited to OpenAI API usage).
• Design and manage scalable inference infrastructure including GPU-aware deployments.
• Implement CI/CD pipelines for LLM deployment and continuous evaluation.
• Monitor system performance including latency throughput token usage cost drift (model and data) and hallucinations.
• Ensure secure compliant and resilient cloud-based model deployments.
• Collaborate with research and engineering for deployments.

Skills:-

• Strong hands-on experience with LLM handling hosting and operationalization.
• Clear understanding of how LLMOps differs from traditional MLOps (prompt management non-deterministic outputs semantic evaluation token economics guardrails etc.).
• Experience with Kubernetes Docker and containerized deployments.
• Cloud expertise (AWS / Azure / GCP) including compute storage IAM networking and monitoring.
• Experience building scalable inference and model-serving architectures.
• Familiarity with tools such as MLflow Kubeflow etc. (good to have).
• Understanding of vector databases RAG systems and evaluation frameworks (preferred).
• Knowledge of GenAI security considerations (prompt injection data leakage prevention).

Qualifications :

• Bachelors degree in Computer Science Engineering or related field.
• DevOps certification (e.g. AWS DevOps Engineer Azure DevOps or equivalent).
• 35 years of experience in MLOps LLMOps ML Engineering or related roles.
• Bachelors or masters degree in computer science Artificial Intelligence Data Science or a related technical field.
• Demonstrated experience deploying ML/LLM systems in production environments.

Remote Work :

No

Employment Type :

Full-time