Senior MLOps Engineer

IFM is hiring a Senior MLOps Engineer in Abu Dhabi, United Arab Emirates. Level rates it ; you can apply on IFM.

Level's read on this job

Level rates this role Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include vLLM, MLflow and Weights And Biases.
Level classifies it as a senior role.
Level lists 43 other open roles at IFM.

AI in this role

vLLMMLflowWeights And BiasesRayElevenlabs

fine-tuningml-opsspeechai-safety

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.

How we rate this

Senior MLOps Engineer at IFM rates 98 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior MLOps Engineer is an open job at IFM.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Abu Dhabi.
The job was posted on November 3, 2025.
Seniority: senior.
Applications are handled by IFM. Apply on IFM

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningML OpsSpeechAI SafetyvLLMMLflowWeights And BiasesRay

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you monitor a model once it's live, and how do you know it needs retraining?
What have you built with speech recognition or text-to-speech, and where did it break?
How do you think about the risk of an AI system in this kind of role failing silently?
Walk me through how you've used vLLM in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Fine-tuning, ML Ops, Speech, AI Safety, and vLLM. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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