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DevOps / MLOps Specialist (LLMOps)

Bosch Global Software Technologies · Bengaluru

CompanyBosch Global Software Technologies
RoleDevOps / MLOps Specialist (LLMOps)
LocationBengaluru
Work modeOn-site
Experience3–6 years
Job typeFull-time
QualificationBachelor's or Master's degree in Computer Science, Engineering, Data Science or a related field

About this job

Bosch Global Software Technologies is hiring a DevOps/MLOps Specialist in Bengaluru to lead MLOps and LLMOps pipelines. You will take machine learning and large language models from training to deployment and monitoring in production.

The role builds CI/CD for ML and LLM models, data and training pipelines, and monitoring for model health, while keeping cost, speed and accuracy in check on cloud GPUs. It also covers model governance, versioning and privacy rules such as GDPR.

Eligibility

  • 5+ years in MLOps, including 2+ years on LLMOps pipelines
  • Experience deploying and optimising ML models in the cloud at scale
  • Docker, Kubernetes and CI/CD with Jenkins, GitLab CI or CircleCI
  • Model serving with TF Serving, TorchServe, Kubeflow or MLflow
  • Terraform or Ansible and strong Python

Skills required

MLOpsLLMOpsKubernetesDockerMLflow / KubeflowAirflowTerraformTensorFlow / PyTorchPrometheus / GrafanaPython

What you will do

  • Design end-to-end MLOps and LLMOps pipelines
  • Automate model deployment with an eye on scale and cost
  • Build CI/CD for ML and LLM models
  • Create monitoring for model performance and health
  • Manage model versions, governance and lifecycle

How to apply

  • Open the official Bosch Global Software Technologies job page with the Apply button and submit your application there.
  • Tailor your resume to ML and LLM pipelines you built, models you served in production and the cost or latency gains.

Before the interview, prepare:

  • LLM serving: batching, quantisation and GPU sizing
  • MLflow or Kubeflow: tracking, registry and promotion to production
  • Model monitoring: drift, quality metrics and alerts
  • CI/CD for models: testing data, models and rollbacks

Apply on official website →

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Posted 10 Nov 2026 · Last checked 10 Nov 2026 · Official source

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