DevOps Engineer (Azure | Terraform | Ansible | Agentic AI for Infra/Monitoring/FinOps) Job at Wissen (Bangalore South)

Job Description

Job Summary: We are looking for a DevOps Engineer with strong hands-on experience in Azure cloud engineering, Infrastructure as Code (Terraform), and configuration management/automation. The role primarily focuses on building reliable cloud infrastructure, enabling CI/CD automation, improving observability and operational excellence, and driving cost optimization (FinOps). Additionally, the engineer will be responsible for designing and developing agentic AI solutions that streamline and support infrastructure operations, monitoring triage, and FinOps insights, leveraging contemporary AI agent frameworks and Azure AI services. The role further encompasses architecting and automating foundational infrastructure, deployment pipelines, monitoring systems, and governance mechanisms essential for ML and GenAI workloads utilizing Azure-native services, Infrastructure as Code (IaC), and advanced MLOps practices. Key responsibilities include enabling orchestration frameworks for LLMs, such as LangGraph, and supporting integration with Claude-based skills and plugins from an infrastructure and platform standpoint. The ideal candidate brings a strong platform and automation mindset, with experience working closely with CCOE’s and Development teams to provide secure, scalable infrastructure automation and agentic solutions.

Job Responsibility

Build, deploy, and manage comprehensive MLOps and LLMOps pipelines on Azure
Design and oversee CI/CD pipelines for machine learning models and large language model workflows utilizing Harness or Azure DevOps
Streamline the promotion of models, prompts, and agent workflows between environments through automation
Establish approval gates, implement rollback mechanisms, and facilitate controlled release processes
Oversee the lifecycle of ML models and LLM-driven workflows, including their training, assessment, deployment, monitoring, and retraining
Administer Azure Machine Learning workspaces, computing resources, environments, model registries, and endpoints
Integrate LLM workflows and agent-centric architectures using LangGraph
Support the incorporation of Claude-based models, skills, and plugins into enterprise-level applications
Operationalize prompt versioning, orchestration strategies, and agent workflows in live production settings
Set up and govern Azure ML and Generative AI infrastructure via Terraform as Infrastructure as Code (IaC)
Standardize development, test, and production environments for ML and GenAI workloads
Automate recurring platform operations and environment setup tasks
Deploy both real-time and batch inference solutions for machine learning and LLM use cases
Enable scalable Generative AI services that are reliable, high-performing, and cost-effective
Apply blue-green or canary deployment techniques where suitable
Implement monitoring practices to track model accuracy, data drift, prompt drift, and agent behavior
Ensure thorough logging, alerting systems, and observability for ML and GenAI platforms
Adhere to enterprise standards concerning security, access controls, and cost management
Collaborate closely with engineering, platform, and development teams
Guide teams toward adopting best practices in MLOps and LLMOps
Contribute to the creation and maintenance of reusable templates, frameworks, and documentation

Requirements

3–5 years of experience in MLOps / ML Engineering / Cloud Engineering
Proficient in designing and deploying end-to-end ML pipelines
Terraform for Azure infrastructure automation
Python for ML, automation, and GenAI workflows
Azure Compute, Storage, Networking, and Identity
Running ML & GenAI workloads at scale on Azure
Supporting data pipelines for ML and LLM workloads
Experience with LangGraph for LLM workflow and agent orchestration
Hands-on exposure to Claude models, including skills/plugins integration
Understanding of prompt management, agent execution, and orchestration patterns
Monitoring, logging, and alerting practices
Troubleshooting production ML/GenAI systems
Cost‑optimized design for compute‑intensive AI workloads

Nice to have

Experience with Responsible AI and enterprise governance
Exposure to multi‑agent architectures
FinOps awareness for ML and GenAI workloads
Experience supporting multiple teams via a shared AI platform

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DevOps Engineer (Azure | Terraform | Ansible | Agentic AI for Infra/Monitoring/FinOps)

Job Description

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