Time since we first saw it
Day 204
Up longer than most. Similar jobs usually come down between day 6 and day 70.
From 4,523 similar postings that came down. Coming down is not always a hire.
Senior MLOps Engineer
- Pay
- Pay not listed
- Work setting
On-site — work setting source
Work setup in the employer’s posting: onsite
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingDesign 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.
From the employer’s posting
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.
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.
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).
Posting record
Report an errorOpen 6 monthsWe first saw this posting 204 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Apr 15, 2026204 days ago
- Recorded sightings
- 144
- Last seen by us
- Nov 5, 2026
- Employer says posted
- Nov 3, 2025
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Ifm-Us is hiring
8 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Its jobs tend to stay up a long time
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- AWS
- Docker
- Grafana
- Kubernetes
- MLflow
- S3
- Terraform
Where the posting names them
- 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).
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
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.
- 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.
- 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.
- 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.
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.
- 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.
- 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.
- 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.
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Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
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.
Employment type
Full-time
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Abu Dhabi
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