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Mahesh Ponnam
Mid-level Data Scientist specializing in credit risk, fraud detection, and ESG analytics
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About
AI/LLM practitioner who has deployed production chatbots across e-commerce, HRMS, and real estate, focusing on retrieval-first workflows for factual tasks like product and property search. Optimized intent understanding and significantly improved latency by using lightweight embeddings and tuning the inference pipeline on Groq (Llama 3.3), while applying modular orchestration and measurable production evaluation.
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Key Strengths
- Built and deployed production chatbots for e-commerce and real estate use cases
- Reduced chatbot latency by optimizing an embeddings-based pipeline on Groq
- Designs modular agent/workflow architectures (routing, retrieval, generation, fallbacks)
- Practical evaluation approach using edge-case queries and production metrics (retrieval accuracy, response quality, user success rates)
- Experience structuring repeatable, monitored AI pipelines using Python workflows and Airflow-style DAG patterns
- Effective collaboration with non-technical stakeholders via rapid prototyping and iterative feedback (tone/accuracy)
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