MLOps Engineer
Posted 3 Nov 2025
Advertised as “Senior MLOps Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
No salary listed for this role.
About the Institute of Foundation Models (IFM)
The Institute of Foundation Models is a dedicated research lab for building, understanding, deploying, and risk-managing large-scale AI systems. We drive innovation in foundation models and their operationalization, empowering research, education, and industry adoption through scalable infrastructure and real-world applications.As part of our engineering team, you will operate at the intersection of machine learning and systems design — building the cloud, orchestration, and deployment layers that power the next generation of intelligent applications at MBZUAI. You’ll work alongside world-class AI researchers and engineers to productionize LLMs, voice models, and multimodal systems at scale.
The Role
As a Senior MLOps Engineer, you will design, build, and maintain robust ML(Machine Learning) infrastructure across training, inference, and deployment pipelines. You will take ownership of the model lifecycle — from data ingestion to real-time serving — and ensure our LLM and speech models are deployed efficiently, securely, and reproducibly in Kubernetes-based environments.This position requires deep hands-on experience with Kubernetes (EKS), Helm, AWS cloud infrastructure, and modern MLOps toolchains (e.g., vLLM, SGLang, OpenWebUI, Weights & Biases, MLflow). Familiarity with speech/voice AI frameworks like ElevenLabs, Whisper, and RVC is also valuable.
Key Responsibilities
- Design and manage scalable ML infrastructure on AWS using EKS, EC2, RDS, S3, and IAM-based access control.
- Build and maintain Kubernetes deployments for LLM and TTS inference using Helm, ArgoCD, and Prometheus/Grafana monitoring.
- Implement and optimize model serving pipelines using vLLM, SGLang, TensorRT, or similar frameworks for high-throughput inference.
- Develop CI/CD and MLOps automation for data versioning, model validation, and deployment (GitHub Actions, Jenkins, or AWS CodePipeline).