Raghu K.

Experience

• 

  Moog Aircraft

  Aug 2018 - Jun 2020

  Elevating aerospace software engineering to new heights at Moog Aircraft, I was pivotal in architecting and optimizing AI-infused software systems that powered advancements in aerospace control technologies.Technical Contributions:• Architected three high-performance software products, integrating sophisticated machine learning algorithms for predictive maintenance, which resulted in a 30% surge in product reliability and a marked increase in lifecycle efficiency for the world's best Aviation OEM enterprise.• Led a diverse team of software engineers through the beta-testing phase of a career development platform, employing advanced analytics and neural network algorithms to dissect and enhance user engagement metrics, thus boosting system functionality by over half.• Conceptualized and implemented a robust neural network-based testing framework, achieving a 30% reduction in test cases and a quarter increase in coverage, setting a new standard for software testing efficacy in flight control systems.• Played a critical role in the design and deployment of next-gen control modules for prominent aircraft models, focusing on algorithmic efficiency and real-time data processing, indicative of a relentless pursuit for engineering perfection.• Mastered and applied a sophisticated tech stack that included Python, Scala, TensorFlow, Scikit-learn, Hadoop, Spark, and CUDA, to perform data engineering tasks on a massive 10TB dataset, thereby revolutionizing data visualization and analytics processes for complex financial portfolios. Show less As a Project Intern, I immersed myself in the design, testing, and optimization of software systems, specializing in AI algorithms and automation processes that set new benchmarks for operational performance in aerospace applications.Technical Achievements:• Innovated a Python-based framework for Verification & Validation processes, cutting down the test cycle from 72 hours to 15 minutes, reflecting the impactful integration of AI in streamlining efficiency.• Championed the development of a scalable base platform for UAVs, enhancing system throughput by 60% through the implementation of concurrent multithreading and Python-C extensions, demonstrating a novel approach to real-time control systems.• Formulated rigorous software testing strategies, ensuring adherence to aerospace software standards DO178B/C, AS9100, and military communication protocols MIL-STD-188 for secure and reliable system operations. Show less

  • Software Engineer

    Jan 2020 - Jun 2020

  • Project Intern

    Aug 2018 - Dec 2019
• 

  Rolls-Royce

  Sept 2020 - Oct 2021

  Research Student

  Real-Time Anomaly Detection and Fault Tolerance: Deployed an ensemble of Isolation Forests, One-Class SVMs, and Autoencoders, reducing false alarms by 25% and increasing flight control system reliability by 30%.Energy Management and Thermal Optimization: Developed a predictive model using regression and neural networks to optimize PM motor performance, reducing thermal output by 15% and extending motor life by 30%.