Work Experience
Senior Machine Learning Engineer
Nov 2023 - Present · 1 yr 2 mos
Leading the development of advanced chatbot solutions and machine learning systems, focusing on automating user interactions and improving information retrieval accuracy using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. Responsible for overseeing MLOps, system design, architecture, and project management from inception to deployment, utilizing AWS and containerization technologies.
Key Projects
Chatbot Solutions for SAP and Toyota
Led the development of advanced chatbot solutions for SAP and Toyota, automating and improving user interactions through LLMs, delivering customized AI-powered interfaces.
Retrieval-Augmented Generation (RAG) System
Led the design and optimization of a RAG system to enhance information retrieval accuracy, increasing MRR by over 30% and Hit Rate by 50%.
Key Achievements
- Increased the Mean Reciprocal Rank (MRR) by over 30% and the Hit Rate by 50% in a Retrieval-Augmented Generation (RAG) system, significantly enhancing information retrieval accuracy.
- Improved client satisfaction by enhancing the precision of search functionalities.
- Successfully deployed open-source LLMs like Llama 2 using vLLM on AWS EC2, scaling the system to support hundreds of concurrent users while ensuring low latency and high throughput.
- Spearheaded the design and migration of a microservices architecture for RAG systems, ensuring scalability and reliability in the client's AWS environment.
Responsibilities
- Led the development of advanced chatbot solutions for major clients, focusing on automation and improving user interactions with LLMs.
- Managed MLOps processes, improving efficiency and productivity within the machine learning team.
- Oversaw the design, architecture, and optimization of ML systems and products, implementing best practices and structured workflows.
- Managed the full lifecycle of ML projects, from planning and development to deployment and monitoring, utilizing AWS.
- Designed and implemented a microservices architecture for RAG systems on AWS, ensuring scalability and reliability.
- Deployed open-source LLMs and scaled them for hundreds of concurrent users while maintaining low latency and high throughput.
- Led the deployment of ML projects by containerizing with Docker, establishing CI/CD pipelines, automating infrastructure setup with Python and AWS CDK, and deploying on AWS.
Skills
Machine Learning Engineer
Jul 2022 - Oct 2023 · 1 yr 4 mos
As a Machine Learning Engineer at Vacon.ai, I developed and deployed cutting-edge solutions in computer vision and NLP, using models like Stable Diffusion, SAM, YOLO, and AWS Rekognition. I worked on projects in virtual renovation, fashion retail, object detection, and document retrieval, enhancing user experiences, streamlining processes, and improving security. My focus was on integrating these models into production environments for scalability and real-world impact.
Key Projects
Virtual Renovation Project
Developed a virtual renovation system for visualizing house renovations using Stable Diffusion for realistic image generation, SAM for precise segmentation, and ControlNet for detailed control over the visual output. This tool provided architects, interior designers, and homeowners with an innovative way to accurately and creatively visualize various design options in a virtual space.
Virtual Try-On System
Created a virtual try-on system for fashion and retail, powered by LORA for Stable Diffusion. This system allowed customers to see how different clothing items looked on them in an immersive, interactive manner. It was designed to enhance user engagement and satisfaction, contributing to improved shopping experiences and conversion rates for retail clients.
Object Detection Systems
Developed a zero-shot object detection system using Grounding DINO and CLIP, enabling the identification of objects without prior training. Additionally, I implemented an object detection pipeline using YOLO, ensuring accurate feature localization and efficient real-time performance for object recognition applications.
Facial Recognition System
Built a facial recognition system using AWS Rekognition, enhancing security and access control. This solution ensured reliable identification and authentication, with high accuracy for security applications in both private and public sectors.
Document Retrieval System
Engineered a document retrieval system using the Haystack model, which facilitated the efficient retrieval of relevant documents from vast corpora in response to user queries. This solution significantly improved information accessibility and streamlined workflows for users in large-scale enterprise environments.
Recruitment Streamlining
Developed a machine learning-based system to extract key information from resumes, such as education, job experience, and skills. This system helped recruiters identify top candidates more efficiently, streamlining the recruitment process and improving hiring outcomes for organizations.
Key Achievements
- Successfully developed and deployed multiple machine learning models in production environments, delivering tangible real-world results for clients.
- Enhanced customer engagement and satisfaction by creating a virtual try-on system that allowed users to see clothing items on themselves, contributing to improved conversion rates in retail.
- Streamlined the recruitment process through a machine learning-driven system for extracting relevant data from resumes, enabling faster candidate identification and decision-making.
- Developed a robust and reliable facial recognition system that improved security and access control with high accuracy, utilizing AWS Rekognition.
- Pioneered a zero-shot object detection system that identified objects without prior training, enabling efficient and scalable object recognition for various use cases.
- Led the development of a virtual renovation tool that allowed users to visualize house renovations accurately and creatively, enhancing design decision-making for architects and homeowners.
Responsibilities
- Design and implement machine learning models for computer vision and NLP projects.
- Conduct experiments and optimize machine learning algorithms to improve system performance.
- Integrate machine learning models into production environments, ensuring scalability and robustness.
- Collaborate with cross-functional teams (designers, architects, product managers) to understand project requirements and deliver innovative solutions.
- Test and validate machine learning solutions for efficiency, accuracy, and real-world applicability.
- Stay up-to-date with the latest trends, research, and technologies in machine learning and AI.
- Ensure the security and reliability of deployed systems, such as facial recognition and object detection.
- Provide mentorship and guidance to junior engineers in machine learning best practices.
Skills
Full Stack Web Developer
Jun 2021 - Jun 2022 · 1 yr 1 mo
As a Full Stack Web Developer at Cipher Coders, I specialized in building dynamic, scalable applications using React.js for the front-end and Node.js with Express.js for the back-end. I developed end-to-end solutions, from creating intuitive UIs to building robust server-side logic, ensuring seamless user experiences and efficient data handling.
Key Projects
Blog Platform with Admin Panel
I developed a full-featured blog platform where users could read, comment, and share posts. The admin panel allowed authenticated users to create, manage, and delete content. Using React.js for the front-end and Node.js/Express.js for the back-end, I integrated MongoDB for content storage and implemented features like authentication, input validation, and secure data handling.
E-commerce Store with Product Management
I worked on an e-commerce platform for managing products, processing orders, and providing a seamless shopping experience. The front-end, built with React.js, allowed users to browse and purchase products, while the back-end in Node.js/Express.js handled user authentication, product inventory, and order processing. I integrated Stripe for secure payment handling. My responsibilities included managing ongoing development, adding new features, and optimizing the platform’s performance and user experience.
Key Achievements
- Successfully launched a fully functional blog platform, increasing user engagement by 30% within the first month of release.
- Integrated Stripe payment gateway in the e-commerce platform, enabling secure transactions.
Responsibilities
- Designed and developed scalable full-stack applications.
- Collaborated with cross-functional teams to deliver high-quality solutions.
- Maintained and enhanced existing applications, adding new features and functionalities.
- Ensured security, performance, and scalability in all aspects of the applications.