LLM, Agentic AI, Langraph, Python

Job Title: AI Ops Engineer

Experience:4+ years relv exp

About the Role

We are seeking a hands-on and proactive AI Ops Engineer to operationalize and

support the deployment of large language model (LLM) workflows, including agentic AI

applications, across Marvell’s enterprise ecosystem.

This role requires strong prompt engineering capabilities, the ability to triage AI pipeline

issues, and a deep understanding of how LLM-based agents interact with tools,

memory, and APIs. You will be expected to diagnose and remediate real-time problems,

from prompt quality issues to model behavior anomalies.

Key Responsibilities

 Design, fine-tune, and manage prompts for various LLM use cases tailored to Marvell’s

enterprise operations.

 Operate, monitor, and troubleshoot agentic AI applications, including identifying whether

issues stem from:

 Prompt quality or structure

 Model configuration or performance

 Tool usage, API failures, or memory/recall issues

 Build diagnostics and playbooks to triage LLM-driven failures, including handling fallback

strategies, retries, or re-routing to human workflows.

 Collaborate with architects, ML engineers, and DevOps to optimize agent orchestration

across platforms like LangGraph, CrewAI, AutoGen, or similar.

 Support integration of agentic systems with enterprise apps like Jira, ServiceNow, Glean, or

Confluence using REST APIs, webhooks, and adapters.

 Implement observability and logging best practices for model outputs, latency, and agent

performance metrics.

 Contribute to building self-healing mechanisms and alerting strategies for production-grade

AI workflows.

Required Qualifications

 3–6 years of experience in software engineering, DevOps, or ML Ops with exposure to

AI/LLM workflows.

 Strong foundation in prompt engineering and experience with LLMs like GPT, Claude,

LLaMA, etc.

 Practical understanding of AIOps platforms or operational AI use cases (incident triage,

log summarization, root cause analysis, etc.).

 Exposure to agentic AI architectures, such as LangGraph, AutoGen, CrewAI, etc.

 Familiarity with scripting (Python), RESTful APIs, and basic system debugging.

 Strong analytical skills and the ability to trace issues across multi-step pipelines and

asynchronous agents.

Good-To-Have

 Glean

 DevRev

 Codium

 Cursor

 Atlassian AI

 Databricks Mosaic AI