Headline

Staff Machine Learning Engineer at Audena AI || UI Guide || MS in AI & ML, LJMU (UK) || Building Scalable AI, ML & Deep Learning Solutions.

About

I am a UI Guide in the University Innovation Fellows program at Stanford University, a global initiative that originated at Stanford and fosters innovation, entrepreneurship, and leadership in higher education. Through this journey, I have developed a strong foundation in driving innovation, mentoring emerging leaders, and leveraging technology to solve real-world challenges. I hold a Master of Science in Artificial Intelligence and Machine Learning from Liverpool John Moores University in the United Kingdom, where I graduated with a strong academic record and conducted research on self-supervised learning for video understanding. With over 5 years of industry experience, I currently work as a Staff Machine Learning Engineer at Audena AI, building scalable, production-grade AI systems across Computer Vision, Generative AI, Large Language Models, Multimodal AI, and Intelligent Automation. My expertise spans the full AI lifecycle from research and model development to deployment, optimization, and large-scale production systems. Throughout my career, I have designed and delivered end-to-end AI solutions for video intelligence, real-time analytics, content automation, multimodal understanding, speech and audio processing, and AI-powered knowledge systems. I have worked extensively with state-of-the-art architectures and frameworks, including Transformers, Vision Transformers, VideoMAE, YOLO, Whisper, GPT, Gemini, and other frontier AI technologies, enabling high-performance systems that operate reliably at scale. My passion lies in transforming complex business challenges into impactful AI solutions that drive efficiency, accelerate decision-making, and create measurable business value. Beyond technical execution, I enjoy mentoring engineers, contributing to innovation ecosystems, and exploring emerging technologies that shape the future of artificial intelligence. Areas of interest include Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, Large Language Models, Computer Vision, Multimodal AI, Cloud Computing, Data Science, Quantum Computing, and Intelligent Systems. I am committed to building AI systems that are not only technically advanced but also practical, scalable, and capable of creating meaningful impact.

Location

Palasa

AI stack

claude code

claude

codex

cursor

gemini

hugging face

Work experience

Staff Machine Learning Engineer at Audena AI, Jun 2026 - Present · 3 mos, Dubai Internet City, Bldg 1 G07, Dubai, UAE, Driving the architecture and development of scalable AI systems powering Audena's AI-first revenue and operations platform. Responsible for technical leadership across Generative AI, LLMs, Agentic AI, Machine Learning Infrastructure, and MLOps. Partnering with cross-functional teams to build enterprise-grade solutions that enhance operational efficiency, automate complex decision-making workflows, and enable intelligent customer interactions across voice and digital channels. Core Areas: Generative AI • LLMs • Agentic AI • Machine Learning • MLOps • Voice AI • Distributed Systems • Production ML • Platform Engineering • Enterprise AI

University Innovation Guide at University Innovation Fellows, Jul 2023 - Present · 3 yrs 2 mos

Software Engineer 2 - AI/ML at VideoVerse, Jan 2026 - May 2026 · 5 mos

AI/ML Engineer at VideoVerse, May 2023 - Feb 2026 · 2 yrs 10 mos, Key Movement Identification and Extraction in Live Sport Events: • Orchestrated the development of a robust pipeline automating key movement identification and extraction in live events, leveraging advanced object detection models such as YOLOv8 with high accuracy. • Implemented cutting-edge video classification algorithms, including CNN-RNN with Multi-Headed Attention and Self-Supervised Contrastive models, alongside Custom VideoMAEv2, enhancing pipeline efficiency significantly. • Spearheaded end-to-end optimization efforts, ensuring seamless and precise identification of crucial movements, thereby optimizing content production processes for enhanced user experience and productivity. Streamlined Analysis: Separating Live Feeds and Replay Segments in Streaming Platforms: • Engineered a model using CNN-RNN architecture to precisely distinguish transitions between live events and corresponding replay segments within streaming feeds. • Developed an Attention-Based Encoder video classification model adept at categorizing video clips into distinct classes, resulting in heightened segmentation accuracy. • Orchestrated the seamless integration of the CNN-RNN model for transition identification and the Attention-based encoder model for video classification, culminating in the establishment of a streamlined process for segregating live feeds and replay segments in streaming environments, achieving high efficiency.

Student at ACM India Winter School at ACM India, Jan 2022 - Jan 2022 · 1 mo, India, I had the privilege of participating in the ACM India Winter School 2021-2022 on Quantum Computing, hosted by IIT Madras and TCS Research. This intensive one-month program provided a comprehensive overview of Quantum Computing, delving into its theoretical foundations, practical applications, and emerging trends. Through engaging lectures, hands-on workshops, and interactive sessions, I gained invaluable insights into the cutting-edge advancements shaping the field of Quantum Computing. This immersive experience not only expanded my knowledge but also fueled my passion for exploring the limitless possibilities of quantum technologies.

Software Engineer at Tavant, Sep 2021 - May 2023 · 1 yr 9 mos, Hyderabad, Telangana, India, Instance Segmentation and Pose Estimation in Video with YOLOv7 Models • Implemented instance segmentation and pose estimation in videos within the computer vision domain, utilizing YOLOv7 models for robust performance. • Developed an end-to-end approach achieving 93% efficiency in Instance Segmentation and subsequent Pose Estimation tasks, enhancing video analysis capabilities. • Spearheaded the integration of YOLOv7 models for precise instance segmentation and pose estimation, facilitating advanced computer vision applications with high accuracy. Data Anonymization and PII Redaction using NLP and NER Techniques • Implemented NLP and NER techniques to anonymize data and redact Personally-Identifying Information (PII) proactively. • Developed a comprehensive solution to anonymize sensitive data and mask PII effectively, ensuring privacy compliance and mitigating data security risks. • Spearheaded the integration of NLP and NER techniques into data storage and analysis workflows, prioritizing privacy protection and confidentiality in data management practices.

University Innovation Fellow at University Innovation Fellows, Mar 2019 - Jul 2023 · 4 yrs 5 mos, Andhra Pradesh, India, As a University Innovation Fellow for the fall 2019 cohort from SRKR Engineering College, I was selected to join the prestigious UIF Program, an initiative of The Hasso Plattner Institute of Design at Stanford University, commonly known as the d.school. During this immersive program, I underwent six weeks of intensive training in Design Thinking, Innovation, and Entrepreneurship, followed by engaging in weekly assignments designed to hone our skills in fostering innovation and entrepreneurship within the university community. In my role as a University Innovation Fellow, I actively collaborated with students, faculty, and administrators to promote a culture of innovation and creative problem-solving on campus. I facilitated workshops, organized events, and spearheaded initiatives aimed at inspiring and empowering students to embrace their entrepreneurial spirit and tackle real-world challenges. Furthermore, I am honored to have been selected as the UI guide for the FALL 2023 batch, where I will serve as a mentor to a group of 4 students from Aditya College of Engineering. In this capacity, I am committed to guiding and nurturing the next generation of innovators and change-makers, ensuring they are equipped with the knowledge and skills to drive innovation and entrepreneurship within their academic community. My dedication to fostering a culture of innovation and empowering students to tackle real-world challenges underscores my commitment to both Stanford University and Aditya College of Engineering. I am privileged to contribute to their ongoing legacy of excellence in innovation and entrepreneurship.

Education

Master of Science - MS, Machine Learning and Artificial Intelligence, Liverpool John Moores University

Advanced Certification in Machine Learning and Cloud, Machine Learning and Cloud, Indian Institute of Technology, Madras

Public links

github: https://github.com/vinayvivek