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Esha Gangam
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps
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About
GenAI/ML engineer from Deloitte who built and shipped a production RAG-based internal search assistant for support teams, delivering quantified operational gains (20% effort reduction, 35% faster manual lookup). Experienced in enterprise-grade LLM reliability (grounding/hallucination control), compliance/security constraints, and rapid release cycles using CI/CD, MLflow, and orchestration tools (Airflow, Databricks Jobs, LangChain).
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Key Strengths
- Built and deployed production RAG internal search assistant at Deloitte
- Delivered measurable impact: reduced support effort 20% and manual lookup time 35%
- Strong grounding/hallucination-mitigation approach (chunking, retrieval tuning, prompt constraints)
- Enterprise-grade security/compliance implementation for approved knowledge sources
- Reliability-focused delivery with CI/CD, versioning, monitoring, and rollback
- End-to-end ML/LLM workflow orchestration using Airflow, Databricks Jobs, and LangChain
- Metrics-driven evaluation and iteration (success metrics, edge-case testing, MLflow tracking, A/B testing)
- Effective collaboration with non-technical stakeholders via demos/prototypes and translating pain points into measurable requirements
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