Wissen Technology - Azure DevOps Engineer - MLOps/LLMOps

Experience: 3 - 5 Years

Location: Bangalore

Mode of Work: Hybrid

Key Responsibilities:

- 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.

Required Skills And Qualification:

1. Core Skills:

- 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

2. Azure & Cloud:

- Azure Compute, Storage, Networking, and Identity

- Running ML & GenAI workloads at scale on Azure

- Supporting data pipelines for ML and LLM workloads

3. GenAI / LLMOps:

- 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

4. DevOps & Operations:

- Monitoring, logging, and alerting practices

- Troubleshooting production ML/GenAI systems

- Cost-optimized design for compute-intensive AI workloads

Good To Have Skills:

- 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

(ref:hirist.tech)