Posted:1 day ago| Platform: Linkedin logo

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On-site

Job Type

Full Time

Job Description

About the Role

We are seeking an experienced MLOps Engineer to design, build, and maintain scalable machine learning infrastructure with strong focus on Azure cloud ecosystem. You will deploy and optimize ML/AI models in production, with emphasis on GPU-accelerated workloads and large language models.

Key Responsibilities

  • Design and implement MLOps pipelines using

    Kubeflow

    and

    KServe

    for model deployment and serving
  • Deploy and manage production ML models on Kubernetes with

    NVIDIA GPU

    acceleration
  • Optimize model inference using

    vLLM, TensorRT-LLM, Triton Inference Server

    , and

    PyTorch Dynamo

  • Implement auto-scaling using

    KEDA, HPA

    , and

    Azure Cluster Autoscaler

  • Build

    CI/CD pipelines

    using

    Azure DevOps

    for automated ML model deployment
  • Manage

    Azure infrastructure

    (AKS, Azure ML, Container Registry) using IaC (Terraform/ARM/Bicep)
  • Implement monitoring and observability using

    Prometheus

    and

    Grafana

  • Perform

    Linux system administration

    and optimize containerized workloads
  • Deploy and manage

    LLM

    applications with

    OpenAI API

    and Azure OpenAI services
  • Monitor GPU utilization, model performance, and implement drift detection
  • Build RAG systems and manage vector databases for LLM applications

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