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Experienced AI Engineer and Data Scientist with 6+ years of experience building and deploying scalable machine learning and Generative AI solutions across enterprise environments. Expertise in LLMs, NLP, and Retrieval-Augmented Generation (RAG), with end-to-end system design from data pipelines to production deployment. Hands-on experience with Transformer-based architectures (BERT, GPT, T5), distributed training on NVIDIA GPUs (A100/V100 with CUDA), and production MLOps using MLflow, CI/CD, Docker, and Kubernetes. Proven ability to deploy scalable AI systems on AWS and GCP (Vertex AI, BigQuery) alongside data engineering with Spark and Kafka.…Experienced AI Engineer and Data Scientist with 6+ years of experience building and deploying scalable machine learning and Generative AI solutions across enterprise environments. Expertise in LLMs, NLP, and Retrieval-Augmented Generation (RAG), with end-to-end system design from data pipelines to production deployment. Hands-on experience with Transformer-based architectures (BERT, GPT, T5), distributed training on NVIDIA GPUs (A100/V100 with CUDA), and production MLOps using MLflow, CI/CD, Docker, and Kubernetes. Proven ability to deploy scalable AI systems on AWS and GCP (Vertex AI, BigQuery) alongside data engineering with Spark and Kafka.

Nagaraj Mangilipalli

AI Engineer, Data Scientist, Web Developer, +2

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Experienced AI Engineer and Data Scientist with 6+ years of experience building and deploying scalable machine learning and Generative AI solutions across enterprise environments. Expertise in LLMs, NLP, and Retrieval-Augmented Generation (RAG), with end-to-end system design from data pipelines to production deployment. Hands-on experience with Transformer-based architectures (BERT, GPT, T5), distributed training on NVIDIA GPUs (A100/V100 with CUDA), and production MLOps using MLflow, CI/CD, Docker, and Kubernetes. Proven ability to deploy scalable AI systems on AWS and GCP (Vertex AI, BigQuery) alongside data engineering with Spark and Kafka.…Experienced AI Engineer and Data Scientist with 6+ years of experience building and deploying scalable machine learning and Generative AI solutions across enterprise environments. Expertise in LLMs, NLP, and Retrieval-Augmented Generation (RAG), with end-to-end system design from data pipelines to production deployment. Hands-on experience with Transformer-based architectures (BERT, GPT, T5), distributed training on NVIDIA GPUs (A100/V100 with CUDA), and production MLOps using MLflow, CI/CD, Docker, and Kubernetes. Proven ability to deploy scalable AI systems on AWS and GCP (Vertex AI, BigQuery) alongside data engineering with Spark and Kafka.

Available to hire

Experienced AI Engineer and Data Scientist with 6+ years of experience building and deploying scalable machine learning and Generative AI solutions across enterprise environments. Expertise in LLMs, NLP, and Retrieval-Augmented Generation (RAG), with end-to-end system design from data pipelines to production deployment.

Hands-on experience with Transformer-based architectures (BERT, GPT, T5), distributed training on NVIDIA GPUs (A100/V100 with CUDA), and production MLOps using MLflow, CI/CD, Docker, and Kubernetes. Proven ability to deploy scalable AI systems on AWS and GCP (Vertex AI, BigQuery) alongside data engineering with Spark and Kafka.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

AI Engineer at Google DeepMind

January 1, 2025 - Present

Architected and deployed large-scale Transformer-based NLP/LLM systems (BERT, GPT, T5), improving search relevance by 25% across high-traffic distributed environments. Designed and fine-tuned LLM-driven conversational AI solutions using PyTorch, TensorFlow, and Hugging Face, implementing attention optimization and prompt engineering. Built production-grade ML pipelines using TFX integrated with GCP Vertex AI for scalable training, validation, and continuous deployment. Leveraged NVIDIA GPUs (A100/V100) with CUDA, cuDNN, and mixed precision (FP16) to reduce training time by 40%+. Deployed and managed models using Docker, Kubernetes (GKE), and TensorFlow Serving for high availability and autoscaling. Optimized inference performance using TensorRT, ONNX Runtime, quantization, and pruning, reducing latency by 30% and compute costs.

Data Scientist at Citius Tech

August 1, 2018 - July 1, 2023

Built and deployed end-to-end ML models using Scikit-learn, XGBoost, and LightGBM, improving performance (AUC/ROC) by 20%+. Developed and fine-tuned LLM-based NLP solutions (BERT, GPT) using PyTorch and Hugging Face with prompt engineering for conversational AI and text analytics. Implemented RAG pipelines integrating LLMs with vector databases (FAISS, Pinecone) to enhance response accuracy and reduce hallucinations. Designed scalable data pipelines using Apache Spark (PySpark) and Kafka for real-time and batch processing. Deployed models using FastAPI, Docker, and Kubernetes, ensuring production-grade scalability. Used AWS (SageMaker) / GCP (Vertex AI, BigQuery) and implemented MLOps (MLflow, CI/CD) for automated training, deployment, and monitoring.