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Amaan Mohammed
Junior AI/ML Engineer specializing in LLM applications and RAG systems
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About
Built and deployed LLM-powered agentic systems including a multi-agent travel planning assistant using LangChain, RAG (FAISS), real-time APIs, and a supervisor agent to manage coordination and reduce hallucinations. Also developed a Text-to-SQL system with schema-aware validation guardrails, and collaborated with drilling domain experts at CNPC USA to build an ML model predicting rate of penetration (ROP).
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Key Strengths
- Built and deployed a production multi-agent travel planning assistant (RAG + real-time APIs + workflow + frontend)
- Improved agent reliability via strict evaluation loops, schema-based outputs, and retrieval grounding to reduce hallucinations
- Designed supervisor-agent pattern to coordinate sub-agents, resolve conflicts, and validate final outputs
- Strong testing/evaluation discipline for agentic workflows (synthetic tests, adversarial prompts, regression testing, metric tracking)
- Implemented Text-to-SQL pipeline with schema extraction, prompt conditioning, and validation/self-correction guardrails
- Effective collaboration with non-technical domain experts to define features, validate outliers, and interpret ML model behavior (CNPC USA drilling/ROP)
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