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Arbaaz Meghani
Senior Machine Learning Engineer specializing in conversational AI and healthcare ML
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ML/AI engineer focused on taking LLM products from experiment to production, with hands-on ownership of a RAG-based customer support system that improved response quality by 35% and cut latency by 30%. Stands out for combining product impact with production rigor across retrieval tuning, safety guardrails, monitoring, and reusable Python/FastAPI services that accelerated adoption across teams.
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Key Strengths
- End-to-end ownership of production LLM systems
- Built and deployed RAG-based customer support automation
- Improved response quality by 35%
- Reduced inference latency by 30%
- Increased customer satisfaction by about 20%
- Strong grounding and hallucination-reduction strategies
- Practical AI safety implementation with moderation and fallback layers
- Production optimization across latency, cost, quality, and safety
- Post-launch iteration using monitoring and feedback loops
- Shared Python platform/service design for cross-team reuse
- Reduced cross-team integration time by 40%
- Improved reliability through standardized APIs, testing, and CI/CD
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