Data Scientist
2 weeks ago
Key Responsibilities
- Design, fine-tune, and manage prompts for diverse enterprise LLM use cases.
Operate, monitor, and troubleshoot agentic AI applications, diagnosing whether issues stem from:
Prompt structure/quality
- Model configuration or performance
Tool usage, API failures, or memory/recall issues
Build diagnostic playbooks for LLM-driven failures, including fallback strategies, retries, and re-routing to human workflows.
- Collaborate with AI architects, ML engineers, and DevOps teams to optimize agent orchestration across platforms such as LangGraph, CrewAI, AutoGen, etc.
- Support integration of agentic systems with enterprise tools (Jira, ServiceNow, Glean, Confluence) via 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 AI workflows.
Required Qualifications
- 36 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 (GPT, Claude, LLaMA, etc.).
- Practical understanding of AIOps platforms or operational AI use cases (incident triage, log summarization, RCA, etc.).
- Exposure to agentic AI architectures: LangGraph, AutoGen, CrewAI, or similar.
- Proficiency in Python scripting, RESTful APIs, and basic system debugging.
- Strong analytical and problem-solving skills to trace issues across multi-step pipelines and asynchronous agents.
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