Associate Manager, AI & Agentic Engineering - AIOps/LLMOps Engineer
Avanade (company site, opens in a new tab)3+ yrsOn-siteDirect career pageEst. 18–32 LPA
Posted 12h ago
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Job description
Structured overview of role & requirements
About This Role
- Build and operate reusable AIOps/LLMOps capabilities that automate the lifecycle of enterprise AI models, prompts, agents, retrieval components, datasets, evaluations, and deployments on Azure.
- Own production delivery foundations: CI/CD, testing, versioning, approvals, rollback, and traceability using Azure DevOps and/or GitHub Actions, APIs, automation, and infrastructure as code.
- Improve AI service quality and operations through observability, incident response, reliability, security and governance controls, capacity planning, and cost optimization.
Minimum Requirements
- Work Experience Required: 3+ years in AI, data, cloud engineering, platform engineering, DevOps, MLOps, or consulting; plus 2+ years hands-on deploying or operating AI/ML or generative AI solutions on Microsoft Foundry/Azure AI Foundry, Azure Machine Learning, or comparable platforms.
- Strong software development and automation skills in Python, PowerShell, Bash, C#, TypeScript, or a comparable language.
- Practical experience with DevOps, MLOps, site reliability engineering, cloud operations, or production application support; working knowledge of AI lifecycle practices including CI/CD, evaluation, observability, reliability, cost management, and incident response.
- Experience with Azure cloud services, APIs, Git, automated testing, infrastructure as code, application monitoring, identity, and secure cloud development. Evening/weekend work may periodically be required; travel may be required based on client demand.
Ideal Candidate Profile
- Has hands-on experience taking AI/ML or generative AI systems beyond prototypes into monitored, supportable production services, ideally on Azure.
- Can bridge software automation, cloud/DevOps operations, and AI lifecycle needs, with attention to quality, cost, safety, security, and traceability.
- May be especially well aligned if experienced in client-facing consulting or systems integration, and with containers, Kubernetes, AI evaluation/tracing, or infrastructure-as-code tools such as Bicep or Terraform.