The Role
Design, build, and maintain scalable ML infrastructure for training and inference of LLM, TTS, and multimodal systems. Own model lifecycle, Kubernetes-based deployments (EKS/Helm), CI/CD, monitoring, cost optimization, infra-as-code (Terraform), and collaborate with researchers to productionize models and observability pipelines.
Summary Generated by Built In
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.
Skills Required
- 4+ years experience in MLOps, DevOps, or Cloud Infrastructure Engineering for ML systems
- Proficiency with Kubernetes (EKS), 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)
- Strong experience with Python, Docker, Git, and CI/CD pipelines
- Strong understanding of model lifecycle management, data pipelines, and observability tools (Grafana, Prometheus, Loki)
- Maintain infrastructure-as-code using Terraform and Helm charts
- Implement CI/CD and MLOps automation (GitHub Actions, Jenkins, or AWS CodePipeline)
- Excellent collaboration skills with ML researchers and software engineers
- Experience with ElevenLabs, Whisper, RVC, 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
- Prior work in LLM deployment, fine-tuning pipelines, or foundation model research
- Exposure to data governance and responsible AI operations
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The Company
What We Do
First a passion, then an idea transformed into success – when it comes to pioneering automation and digitalisation technology, the ifm group is the ideal partner. Since its foundation in 1969, ifm has developed, produced and sold sensors, controllers, software and systems for industrial automation and for SAP-based solutions for supply chain management and shop floor integration worldwide. As one of the pioneers of Industry 4.0, ifm develops and implements consistent solutions to digitalise the entire value chain “from sensor to ERP”. Today, the second-generation family-run ifm group has more than 8,750 employees and is one of the worldwide market leaders. The group combines the internationality and innovative strength of a growing group of companies with the flexibility and close customer contact of a medium-sized company.
