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AI/ML Engineer with ~4 years of experience designing and deploying machine learning, Generative AI, and LLM-powered solutions. Strong expertise in PyTorch, TensorFlow, NLP, Retrieval-Augmented Generation (RAG), vector search, and AI agent workflows. Experienced building production-ready inference pipelines, semantic search systems, and MLOps workflows with AWS, monitoring, drift detection, and continuous evaluation to ensure reliable, high-performance outcomes.…AI/ML Engineer with ~4 years of experience designing and deploying machine learning, Generative AI, and LLM-powered solutions. Strong expertise in PyTorch, TensorFlow, NLP, Retrieval-Augmented Generation (RAG), vector search, and AI agent workflows. Experienced building production-ready inference pipelines, semantic search systems, and MLOps workflows with AWS, monitoring, drift detection, and continuous evaluation to ensure reliable, high-performance outcomes.

Dilip Pushadapu

AI Engineer, Data Scientist, Programmer, +2

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AI/ML Engineer with ~4 years of experience designing and deploying machine learning, Generative AI, and LLM-powered solutions. Strong expertise in PyTorch, TensorFlow, NLP, Retrieval-Augmented Generation (RAG), vector search, and AI agent workflows. Experienced building production-ready inference pipelines, semantic search systems, and MLOps workflows with AWS, monitoring, drift detection, and continuous evaluation to ensure reliable, high-performance outcomes.…AI/ML Engineer with ~4 years of experience designing and deploying machine learning, Generative AI, and LLM-powered solutions. Strong expertise in PyTorch, TensorFlow, NLP, Retrieval-Augmented Generation (RAG), vector search, and AI agent workflows. Experienced building production-ready inference pipelines, semantic search systems, and MLOps workflows with AWS, monitoring, drift detection, and continuous evaluation to ensure reliable, high-performance outcomes.

Available to hire

AI/ML Engineer with ~4 years of experience designing and deploying machine learning, Generative AI, and LLM-powered solutions. Strong expertise in PyTorch, TensorFlow, NLP, Retrieval-Augmented Generation (RAG), vector search, and AI agent workflows.

Experienced building production-ready inference pipelines, semantic search systems, and MLOps workflows with AWS, monitoring, drift detection, and continuous evaluation to ensure reliable, high-performance outcomes.

Skills

Language

Work Experience

AI/ML Engineer at NVIDIA

September 1, 2025 - Present

Built a FAISS-based Retrieval-Augmented Generation (RAG) system enabling semantic retrieval across ~1.5M documents and system logs, improving contextual grounding for LLM workflows. Enhanced retrieval quality and efficiency using hybrid search (BM25 + dense embeddings) and re-ranking models. Improved LLM inference performance by using TensorRT and CUDA acceleration, reducing transformer latency by 30% in distributed GPU serving. Fine-tuned LLaMA models with LoRA/PEFT for domain adaptation to improve structured extraction and reasoning. Developed LangChain-style orchestration and RAG pipelines to automate summarization and classification for ~70K monthly operational queries. Implemented evaluation and observability for deployed ML/LLM systems, tracking hallucination rate, Recall@K, drift detection, and GPU utilization across 8+ models.

Machine Learning Engineer at LTIMindtree

January 1, 2022 - July 1, 2024

Designed fraud pattern detection using PyTorch and scikit-learn on 1.8–2.5M daily transaction logs to identify suspicious behaviors across payment channels. Built credit default prediction with XGBoost and TensorFlow on 40M+ customer lending records to support risk-tier segmentation. Implemented customer grievance classification with Hugging Face BERT, categorizing 300K monthly complaints for faster routing. Deployed real-time risk scoring inference services using TensorFlow/PyTorch supporting 150–300 requests per second with batch processing for low latency. Built ETL and feature engineering pipelines with PySpark and AWS S3 to create consistent training datasets. Developed AML alert prioritization using gradient boosting to reduce manual review queue by 20%. Delivered SQL/Power BI dashboards for drift/accuracy/feature stability monitoring for compliance and audit readiness.