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Hanif Lashari
Mid-level Data & Machine Learning Engineer specializing in anomaly detection and forecasting
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About
Built and productionized an agentic RAG assistant using Ollama + LangChain + MCP + ChromaDB to speed up and standardize access to operational knowledge from tickets and runbooks. Focused on real-world reliability: mitigated timeouts/latency with retries and concurrency limits, improved retrieval via chunking/embedding iteration, and reduced hallucinations through citation-grounding and confidence-based abstention. Also partnered with non-technical ops staff to deliver anomaly detection/monitoring by translating operational needs into model signals, thresholds, and alerting logic.
Experience
Education
Key Strengths
- Built and deployed an agentic RAG assistant over operational tickets/runbooks
- Stabilized LLM pipeline reliability via timeouts/retries and concurrency controls
- Improved retrieval quality through iterative chunking/embedding tuning and parameter tuning (e.g., top-K)
- Reduced hallucinations with strict grounding (citations), confidence thresholds, and safe abstention behavior
- Designed workflows as testable components (ingestion, retrieval, validation, generation) with offline test sets
- Measures and improves production performance using latency, retrieval relevance, and answerability metrics with step-level logging
- Effective collaboration with non-technical operational stakeholders using plain language and simple visualizations
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