Senior MLOps Engineer

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). Integrate OpenWebUI , Gradio , or similar UIs for user-facing model demos and internal evaluation tools. Collaborate with ML researchers to productize models — including TTS (e.g., ElevenLabs API), ASR (Whisper), and LLM-based chat systems. Ensure observability, cost optimization, and reliability of cloud resources across multiple environments. Contribute to internal tools for dataset curation, model monitoring, and retraining pipelines . Maintain infrastructure-as-code using Terraform and Helm charts for reproducibility and governance. Support real-time multimodal workloads (voice, text, vision) across inference clusters.

Academic Qualifications 4+ years of experience in MLOps , DevOps , or Cloud Infrastructure Engineering for ML systems. Strong proficiency in Kubernetes , Helm , and container orchestration . Experience deploying ML models via vLLM , SGLang , TensorRT , or Ray Serve . Proficiency with AWS services (EKS, EC2, S3, RDS, CloudWatch, IAM). Solid experience with Python , Docker , Git , and CI/CD pipelines . Strong understanding of model lifecycle management , data pipelines , and observability tools (Grafana, Prometheus, Loki). Excellent collaboration skills with ML researchers and software engineers.

Professional Experience – Preferred Extensive Experience with vLLM, K8s, Elevenlabs , Whisper , Gradio/OpenWebUI , or custom TTS/ASR model hosting. Familiarity with multi-GPU scheduling , NCCL optimization , and HPC cluster integration . Knowledge of security , cost management , and network policy in multi-tenant Kubernetes clusters and cloudflare systems. Prior work in LLM deployment , fine-tuning pipelines , or foundation model research . Exposure to data governance and responsible AI operations in research or enterprise settings.