About Job/Role

Job Title: AI Ops Engineer
Experience: 3–6 Years
Location: Pune

We are looking for a hands-on AI Ops Engineer to join our team and support the deployment and operational stability of LLM-powered agentic applications across Marvell’s enterprise ecosystem. This role sits at the intersection of AI observability, DevOps, and prompt engineering, and plays a key part in enabling reliable and scalable AI systems in production.

If you're passionate about building robust AI workflows and enjoy debugging, optimizing, and making AI tools production-ready, this is a great opportunity to work on cutting-edge technologies like LangGraph, CrewAI, and AutoGen.

- Roles and Responsibilities

Design, fine-tune, and manage LLM prompts for enterprise use cases.
Operate, monitor, and troubleshoot agentic AI applications, identifying root causes related to:
- Prompt structure and quality
- Model behavior or performance
- API/tool failures or memory/recall issues
Build diagnostic tools and playbooks to support triaging and resolving LLM-related issues.
Implement fallback strategies, retries, and escalation to human workflows when needed.
Collaborate with architects, ML engineers, and DevOps teams to optimize agent orchestration.
Support integrations between agentic AI systems and enterprise tools like Jira, ServiceNow, Glean, and Confluence using REST APIs, webhooks, and adapters.
Apply observability best practices for tracking model outputs, latency, and agent performance.
Contribute to developing self-healing mechanisms, alerts, and logging for production-grade AI systems.

- Required Qualification

3–6 years of experience in software engineering, DevOps, or ML Ops with exposure to AI/LLM workflows.
Proficiency in prompt engineering and hands-on experience with LLMs such as GPT, Claude, or LLaMA.
Understanding of AIOps platforms and practical experience in AI-driven incident triage, log summarization, or root cause analysis.
Familiarity with agentic AI architectures like LangGraph, AutoGen, CrewAI, etc.
Comfortable with Python scripting, RESTful APIs, and basic system-level debugging.
Strong analytical and debugging skills to trace issues across multi-step pipelines and async agents.

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