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Piyush Rajendra
Mid-level AI/ML Engineer specializing in production RAG systems and MLOps
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Built and deployed a GPT-4 + Pinecone RAG system that lets users query large internal document collections with grounded, cited answers. Demonstrates strong applied LLM engineering (chunking experiments, hallucination controls, metadata recency boosting) plus production-minded evaluation/monitoring and performance tuning (rate-limit mitigation via pooling/batching). Also effective at translating complex AI concepts to non-technical stakeholders through prototypes and live demos, helping secure client sponsorship.
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Key Strengths
- Built and deployed a production RAG system for querying large document collections
- Practical mitigation of LLM context window limits via tested chunking strategy (256/512/1024; selected 512)
- Hallucination reduction through citation grounding with stored source metadata and constrained generation
- Operational ownership: weekly monitoring of latency/accuracy and systematic review of failed queries
- Workflow orchestration using LangChain chains integrating retrieval, GPT-4 generation, and citation formatting
- Performance optimization under vector DB rate limits using connection pooling and batch queries
- Reliability approach using clear success criteria and real-world A/B testing
- Stakeholder communication: explains LLM/RAG concepts (hallucinations, vector DBs) with demos and simple examples to secure client buy-in
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