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Jayaprakash Majji
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and scalable inference
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About
ML/LLM engineer who built and shipped an LLM-powered internal knowledge assistant at Meta, focusing on production-grade RAG to reduce hallucinations and improve trust. Deep experience with scaling and serving (FSDP/DeepSpeed/LoRA, Triton, Kubernetes autoscaling) and reliability practices (Airflow retraining, MLflow versioning, monitoring with rollback), including sub-100ms latency and ~35% GPU memory reduction.
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Key Strengths
- Built and deployed LLM-powered internal knowledge assistant in production at Meta
- Mitigated hallucinations using RAG (FAISS/LlamaIndex) plus prompt guardrails and automated factuality/toxicity evaluation
- Scaled/fine-tuned LLMs with FSDP, DeepSpeed, and LoRA; reduced GPU memory usage ~35%
- Achieved sub-100ms inference latency via quantization, adapters, and Triton inference server with batching
- Production ML reliability: Airflow retraining, MLflow versioning, continuous monitoring and automated rollback
- Strong evaluation-driven development: offline benchmarks, edge-case testing, A/B tests, canary deployments
- Effective cross-functional collaboration translating non-technical requirements into measurable ML/system metrics
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