Senior MLOps Engineer بدوام كامل في أبوظبي
التخصص
متفرقات
نوع الدوام
دوام كامل
الخبرة
أي مستوى
الموقع
أبوظبي
وصف الوظيفة
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 RoleAs 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 ResponsibilitiesDesign and manage scalable ML infrastructure on AWS using EKS, EC2, RDS, S3, and IAM-based access controlBuild and maintain Kubernetes deployments for LLM and TTS inference using Helm, ArgoCD, and Prometheus/Grafana monitoringImplement and optimize model serving pipelines using vLLM, SGLang, TensorRT, or similar frameworks for high-throughput inferenceDevelop 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 toolsCollaborate with ML researchers to productize models — including TTS (e.g., ElevenLabs API), ASR (Whisper), and LLM-based chat systemsEnsure observability, cost optimization, and reliability of cloud resources across multiple environmentsContribute to internal tools for dataset curation, model monitoring, and retraining pipelinesMaintain infrastructure-as-code using Terraform and Helm charts for reproducibility and governanceSupport real-time multimodal workloads (voice, text, vision) across inference clustersAcademic Qualifications4+ years of experience in MLOps, DevOps, or Cloud Infrastructure Engineering for ML systemsStrong proficiency in Kubernetes, Helm, and container orchestrationExperience deploying ML models via vLLM, SGLang, TensorRT, or Ray ServeProficiency with AWS services (EKS, EC2, S3, RDS, CloudWatch, IAM)Solid experience with Python, Docker, Git, and CI/CD pipelinesStrong understanding of model lifecycle management, data pipelines, and observability tools (Grafana, Prometheus, Loki)Excellent collaboration skills with ML researchers and software engineersProfessional Experience – PreferredExtensive Experience with vLLM, K8s, Elevenlabs, Whisper, Gradio/OpenWebUI, or custom TTS/ASR model hostingFamiliarity with multi-GPU scheduling, NCCL optimization, and HPC cluster integrationKnowledge of security, cost management, and network policy in multi-tenant Kubernetes clusters and cloudflare systemsPrior work in LLM deployment, fine-tuning pipelines, or foundation model researchExposure to data governance and responsible AI operations in research or enterprise settings
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