Description

You will operationalize and support the deployment of large language model (LLM) workflows, including agentic AI applications, across the enterprise.

Responsibilities

• Design, fine-tune, and manage prompts for various LLM use cases tailored to enterprise operations.
• Operate, monitor, and troubleshoot agentic AI applications, diagnosing issues related to prompt quality, model configuration, tool usage, or API failures.
• Build diagnostics and playbooks to triage LLM-driven failures, implementing fallback strategies or re-routing to human workflows.
• Collaborate with architects and ML engineers to optimize agent orchestration across platforms like LangGraph, CrewAI, or AutoGen.
• Support integration of agentic systems with enterprise apps like Jira, ServiceNow, Glean, or Confluence using REST APIs and webhooks.

Required Skills

• 3–6 years of experience in software engineering, DevOps, or ML Ops with AI/LLM workflow exposure.
• Strong foundation in prompt engineering and experience with LLMs such as GPT, Claude, or LLaMA.
• Practical understanding of AIOps platforms or operational AI use cases (e.g., incident triage, root cause analysis).
• Exposure to agentic AI architectures, including LangGraph, AutoGen, or CrewAI.
• Familiarity with scripting in Python and working with RESTful APIs.
• Strong analytical skills to trace issues across multi-step pipelines and asynchronous agents.
• Experience integrating systems using Jira, ServiceNow, Glean, or Confluence.

Key Skills

Education

Any Graduate