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Moses Immanuel
Mid-level Data Scientist specializing in machine learning and big data analytics
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About
Walmart engineer who built and shipped a production LLM+RAG system to automate triage and analysis of computer support chats/tickets, producing grounded, schema-constrained JSON outputs for summaries, urgency, and routing recommendations. Emphasizes reliability (hallucination control, confidence thresholds, human-in-the-loop) and runs end-to-end pipelines with Airflow and AWS-native orchestration, plus rigorous evaluation and monitoring tied to business KPIs.
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Key Strengths
- Built and deployed an LLM-assisted production system for support conversation triage/analysis at Walmart
- Mitigates hallucinations via strict JSON schemas, enums-only outputs, and RAG grounding on approved internal documents
- Designs reliable agent/workflow systems with validation, safe fallbacks, and confidence-based routing to manual review
- Handles long/noisy conversation inputs using hierarchical summarization and chunking to fit context limits
- Production orchestration expertise across Airflow and AWS-native workflows (EventBridge/Lambda/Step Functions/ECS/EMR)
- Evaluation-driven iteration: unit tests, golden sets, adversarial tests, shadow/A-B tests tied to business KPIs
- Strong cross-functional delivery with non-technical stakeholders; defines success metrics and builds dashboards to drive adoption
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