Senior Staff Engineer (DevOps Engineer- AI/ML)

Nagarro
US
RemoteCareer-pivot friendly

Why this role

Pace
Fast Paced
The fast-paced nature of this role is evident from the requirement to enable fast iteration and reliable deployment of models and services, indicating a need for continuous improvement and adaptation.
Collaboration
Medium
Collaboration is a key aspect of this role, as the engineer will be supporting the deployment and monitoring of AI models and APIs in production, which requires coordination with other teams.
Autonomy
Medium
The job demands a high level of autonomy, as the engineer is expected to design and maintain CI/CD pipelines and automate infrastructure provisioning independently.
Decision Impact
Team
Decisions made by the engineer will have a significant impact on the reliability and scalability of the GenAI platform, as well as the efficiency of model deployment and monitoring.
Role Level
Individual Contributor
The complexity of this role is high, given the need to manage multiple tools and technologies, such as Jenkins, GitHub Actions, Terraform, Kubernetes, and MLflow.
Career Pivot Friendly
Welcomes transferable skills
Individuals with experience in software development or systems engineering can transition into this role, leveraging their technical skills and knowledge of automation and cloud-native practices.

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • enable fast iteration and reliable deployment of models and services
  • support deployment and monitoring of LLM-based models and APIs in production
Typical background
hands-on experience with CI/CD toolsproficiency with cloud platforms

Transferable backgrounds

  • Coming from DevOps Engineer at a tech startup
    CI/CD pipeline design · cloud-native practices
    The experience in designing and maintaining CI/CD pipelines and familiarity with cloud-native practices will directly transfer to this role.
  • Coming from Data Scientist in a financial institution
    ML model deployment · observability tools
    Experience with deploying machine learning models and using observability tools like Prometheus and Grafana will be highly relevant.

Skills & requirements

Required

Ci/cd PipelinesInfrastructure ProvisioningContainer OrchestrationMlops WorkflowsDeployment And MonitoringJenkinsGithub ActionsTerraformKubernetesDockerCloud-native PracticesBashPythonMlflowCloud Platforms

Preferred

Llm/genai Deployment WorkflowsModel Performance MonitoringObservability ToolsSecurity And Cost Optimisation Best Practices For ML Infrastructure

Stack & domain

JenkinsGithub ActionsTerraformKubernetesDockerCloud-native PracticesMlflowAzure MLBashPythonLLMGenaiPrometheusGrafanaSecurity Best PracticesCost Optimisation Best PracticesAutomation SkillsMonitoring SkillsDeployment SkillsDevOpsMlopsCloud PlatformsAzureAWSGCP

About the role

This role involves designing and maintaining CI/CD pipelines and automating infrastructure provisioning for a GenAI platform, making it ideal for a DevOps engineer with a strong background in cloud-native practices and MLOps.

Original posting from Nagarro

Job Description

Looking for a DevOps/MLOps Engineer to build and manage scalable automated infrastructure for our LLM‑powered GenAI platform. You’ll enable fast iteration and reliable deployment of models and services through robust CI/CD pipelines, container orchestration and ML lifecycle tooling.

Key Responsibilities

  • Design and maintain CI/CD pipelines using Jenkins, GitHub Actions or similar.
  • Automate infrastructure provisioning using Terraform and manage services with Kubernetes.
  • Write and maintain Bash/Python scripts for automation and operational tooling.
  • Implement and monitor MLOps workflows using tools like MLflow, Azure ML or similar.
  • Support deployment and monitoring of LLM‑based models and APIs in production.

Required Skills

  • Hands‑on experience with Jenkins, GitHub Actions or equivalent CI/CD tools.
  • Proficiency with Terraform, Kubernetes, Docker and cloud‑native practices.
  • Strong scripting skills in Bash and Python.
  • Experience with ML model tracking, versioning and deployment using MLflow or similar.
  • Familiarity with cloud platforms (e.g. Azure, AWS or GCP).

Nice to Have

  • Exposure to LLM/GenAI deployment workflows.
  • Experience with model performance monitoring and observability tools (Prometheus, Grafana, etc.).
  • Security and cost optimisation best practices for ML infrastructure.

Remote Work

Yes

Employment Type

Full‑time

Source: Nagarro careers

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