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AI/ML Engineer with 3+ years of experience building and deploying scalable machine learning and Generative AI solutions in production environments. Strong expertise in Python, SQL, PyTorch, TensorFlow, and gradient-boosting frameworks, with hands on experience developing endto-end ML pipelines using Kubeflow, MLflow, and AWS. Proven ability to improve model performance (F1, AUC), reduce inference latency, and optimize infrastructure costs through ONNX quantization and containerized deployments with Docker and Kubernetes. Experienced in transformer architectures, Retrieval-Augmented Generation (RAG), large scale data processing with Spark, and developing high performance REST APIs using FastAPI.…AI/ML Engineer with 3+ years of experience building and deploying scalable machine learning and Generative AI solutions in production environments. Strong expertise in Python, SQL, PyTorch, TensorFlow, and gradient-boosting frameworks, with hands on experience developing endto-end ML pipelines using Kubeflow, MLflow, and AWS. Proven ability to improve model performance (F1, AUC), reduce inference latency, and optimize infrastructure costs through ONNX quantization and containerized deployments with Docker and Kubernetes. Experienced in transformer architectures, Retrieval-Augmented Generation (RAG), large scale data processing with Spark, and developing high performance REST APIs using FastAPI.

Guna Shekar Daggupati

Data Scientist, AI Engineer, Full Stack Developer, +1

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AI/ML Engineer with 3+ years of experience building and deploying scalable machine learning and Generative AI solutions in production environments. Strong expertise in Python, SQL, PyTorch, TensorFlow, and gradient-boosting frameworks, with hands on experience developing endto-end ML pipelines using Kubeflow, MLflow, and AWS. Proven ability to improve model performance (F1, AUC), reduce inference latency, and optimize infrastructure costs through ONNX quantization and containerized deployments with Docker and Kubernetes. Experienced in transformer architectures, Retrieval-Augmented Generation (RAG), large scale data processing with Spark, and developing high performance REST APIs using FastAPI.…AI/ML Engineer with 3+ years of experience building and deploying scalable machine learning and Generative AI solutions in production environments. Strong expertise in Python, SQL, PyTorch, TensorFlow, and gradient-boosting frameworks, with hands on experience developing endto-end ML pipelines using Kubeflow, MLflow, and AWS. Proven ability to improve model performance (F1, AUC), reduce inference latency, and optimize infrastructure costs through ONNX quantization and containerized deployments with Docker and Kubernetes. Experienced in transformer architectures, Retrieval-Augmented Generation (RAG), large scale data processing with Spark, and developing high performance REST APIs using FastAPI.

Available to hire

AI/ML Engineer with 3+ years of experience building and deploying scalable machine learning and Generative AI solutions in production
environments. Strong expertise in Python, SQL, PyTorch, TensorFlow, and gradient-boosting frameworks, with hands on experience developing endto-end ML pipelines using Kubeflow, MLflow, and AWS. Proven ability to improve model performance (F1, AUC), reduce inference latency, and
optimize infrastructure costs through ONNX quantization and containerized deployments with Docker and Kubernetes. Experienced in transformer
architectures, Retrieval-Augmented Generation (RAG), large scale data processing with Spark, and developing high performance REST APIs using
FastAPI.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI/ML Engineer at Amgen

June 1, 2025 - Present

Led end-to-end ML pipelines with Python, Kubeflow, and AWS SageMaker, automating data ingestion and model deployment to 99.9% uptime and 28% faster training. Containerized PyTorch/TensorFlow models with Docker/Kubernetes and exposed FastAPI REST endpoints for real-time clinical predictions with sub-100ms latency (10k+ inferences/hour). Optimized transformer and gradient-boosting models (XGBoost, LightGBM) with ONNX quantization and pruning to reduce inference costs by 45% while improving F1-score by 6%. Performed large-scale EDA on genomics/clinical data using Pandas/Spark, engineered domain features, and implemented Retrieval-Augmented Generation (RAG) pipelines to automate clinical document extraction with 30% manual review reduction while HIPAA compliant.

AI/ML Engineer at TCS

August 1, 2022 - December 1, 2023

Automated end-to-end ML workflows with Python, MLflow, Kubeflow, and AWS SageMaker, delivering CI/CD‑integrated deployment and 99% uptime; reduced model release cycles by 30% via retraining and monitoring. Designed and fine-tuned models for BFSI fraud detection and chatbot automation using PyTorch, TensorFlow, and XGBoost, achieving F1 > 90% and lowering false positives by 18%. Built containerized ML microservices with Docker/Kubernetes and FastAPI endpoints for rapid deployment and MTTR < 2 hours. Implemented Generative AI solutions with RAG pipelines and text embeddings on financial datasets to automate document processing with sub-500ms latency. Created SQL/Spark ETL pipelines improving data freshness to under 24 hours and increasing feature reuse by ~70%, with drift monitoring to maintain SLA compliance.

ML Engineer at Cimpress India

March 1, 2021 - July 1, 2022

Developed computer vision and recommendation models powering design personalization and print quality prediction, achieving precision/recall above 0.90 and reducing manual review by 25%. Built multimodal data pipelines (images, text, order history) with SQL, Spark, and ETL to keep data freshness under 24 hours and boost feature reuse by ~60%. Productionized prototypes as Dockerized microservices on AWS (EC2/SageMaker) with FastAPI endpoints enabling real-time design suggestions at under 200ms during peak traffic. Automated training/deployment with MLflow, Kubeflow, and CI/CD, maintaining 99.5% pipeline reliability and bi-weekly releases. Applied ONNX-based compression to cut GPU usage by 40% while preserving F1/AUC. Collaborated with cross-functional teams in Agile sprints to drive improvements in order conversion.