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I am an AI Engineer and AWS Certified ML Associate specializing in advanced AI technologies such as Generative AI, Retrieval-Augmented Generation (RAG) systems, and optimized GPU kernels for scalable, real-time solutions. Throughout my career, I've contributed to significant performance improvements, including a 70% reduction in CANalyzer simulation time through production-grade RAG pipelines. I have experience in designing intelligent systems, building AI pipelines, managing cloud deployments, and leading small teams to develop adaptive solutions. Currently, I am working as a Research Assistant at the University at Buffalo, focusing on reinforcement learning-based beam alignment for 5G signals. My past roles include AI engineering internships at Bosch Global Software Technologies, where I developed and deployed edge device computer vision pipelines and AI troubleshooting assistants. I thrive in collaborative environments that allow me to apply my extensive skills in AI, machine learning, cloud computing, and software development to solve complex real-world problems.…I am an AI Engineer and AWS Certified ML Associate specializing in advanced AI technologies such as Generative AI, Retrieval-Augmented Generation (RAG) systems, and optimized GPU kernels for scalable, real-time solutions. Throughout my career, I've contributed to significant performance improvements, including a 70% reduction in CANalyzer simulation time through production-grade RAG pipelines. I have experience in designing intelligent systems, building AI pipelines, managing cloud deployments, and leading small teams to develop adaptive solutions. Currently, I am working as a Research Assistant at the University at Buffalo, focusing on reinforcement learning-based beam alignment for 5G signals. My past roles include AI engineering internships at Bosch Global Software Technologies, where I developed and deployed edge device computer vision pipelines and AI troubleshooting assistants. I thrive in collaborative environments that allow me to apply my extensive skills in AI, machine learning, cloud computing, and software development to solve complex real-world problems.

Maria Nivetha Antony Pushparaj

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I am an AI Engineer and AWS Certified ML Associate specializing in advanced AI technologies such as Generative AI, Retrieval-Augmented Generation (RAG) systems, and optimized GPU kernels for scalable, real-time solutions. Throughout my career, I've contributed to significant performance improvements, including a 70% reduction in CANalyzer simulation time through production-grade RAG pipelines. I have experience in designing intelligent systems, building AI pipelines, managing cloud deployments, and leading small teams to develop adaptive solutions. Currently, I am working as a Research Assistant at the University at Buffalo, focusing on reinforcement learning-based beam alignment for 5G signals. My past roles include AI engineering internships at Bosch Global Software Technologies, where I developed and deployed edge device computer vision pipelines and AI troubleshooting assistants. I thrive in collaborative environments that allow me to apply my extensive skills in AI, machine learning, cloud computing, and software development to solve complex real-world problems.…I am an AI Engineer and AWS Certified ML Associate specializing in advanced AI technologies such as Generative AI, Retrieval-Augmented Generation (RAG) systems, and optimized GPU kernels for scalable, real-time solutions. Throughout my career, I've contributed to significant performance improvements, including a 70% reduction in CANalyzer simulation time through production-grade RAG pipelines. I have experience in designing intelligent systems, building AI pipelines, managing cloud deployments, and leading small teams to develop adaptive solutions. Currently, I am working as a Research Assistant at the University at Buffalo, focusing on reinforcement learning-based beam alignment for 5G signals. My past roles include AI engineering internships at Bosch Global Software Technologies, where I developed and deployed edge device computer vision pipelines and AI troubleshooting assistants. I thrive in collaborative environments that allow me to apply my extensive skills in AI, machine learning, cloud computing, and software development to solve complex real-world problems.

Available to hire

I am an AI Engineer and AWS Certified ML Associate specializing in advanced AI technologies such as Generative AI, Retrieval-Augmented Generation (RAG) systems, and optimized GPU kernels for scalable, real-time solutions. Throughout my career, I’ve contributed to significant performance improvements, including a 70% reduction in CANalyzer simulation time through production-grade RAG pipelines. I have experience in designing intelligent systems, building AI pipelines, managing cloud deployments, and leading small teams to develop adaptive solutions.

Currently, I am working as a Research Assistant at the University at Buffalo, focusing on reinforcement learning-based beam alignment for 5G signals. My past roles include AI engineering internships at Bosch Global Software Technologies, where I developed and deployed edge device computer vision pipelines and AI troubleshooting assistants. I thrive in collaborative environments that allow me to apply my extensive skills in AI, machine learning, cloud computing, and software development to solve complex real-world problems.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

Amharic

Intermediate

Afar

Beginner

Work Experience

Research Assistant at University at Buffalo, The State University of New York

February 1, 2025 - Present

Designing reinforcement learning-based beam alignment system for 5G signals in massive MIMO environments using contextual bandit modeling techniques.

AI Engineer Intern at Bosch Global Software Technologies (BGSW)

May 1, 2023 - September 4, 2025

Built advanced driver-assistance systems (ADAS) computer vision pipelines on embedded edge devices utilizing Kalman filters, edge detection, and transformer-based models for improved lane detection accuracy by 30% in real-time driving scenarios. Designed and deployed a large language model-based troubleshooting assistant with RAG and FAISS on AWS platforms, reducing diagnostic time by 70%. Led a 2-member team to develop an adaptive battery alert system using signal processing (Wavelet, FFT) on over 1 million sensor data samples, utilizing AWS serverless and storage services to enhance prediction accuracy.