Ankit
Ellenabad, Hisar Division, Haryana, India
Experience: 3 years
Open to: Full-Time
Education: Bachelors
Availability: Within 30 Days
Skills: AWS, GCP, Git, Go, Jupyter Notebooks, LLMs/ChatGPT, MongoDB, MySQL, NumPy, pandas, Postman, Python3, PyTorch, scikit-learn
Previously worked at: IT Service Companies
Assessment Score: 100
- Comprehensive AI Engineering Experience
A seasoned AI Engineer with 3 years of experience specializing in building scalable machine learning models, deep learning architectures (CNN, RNN), and implementing cutting-edge AI solutions. Proficient in technologies like Generative AI, LLMs, and NLP, delivering innovative solutions to complex business challenges. - Academic Background from IIT Roorkee
Holds a Bachelor’s degree in Electrical Engineering from IIT Roorkee, complemented by hands-on experience in machine learning and AI projects. Developed a robust foundation in programming, data analysis, and software development, consistently achieving strong academic and practical results. - Expertise in Scalable AI Applications and Cloud Integration
Successfully designed and deployed AI-driven projects, such as Britannia Bourbon Biscuit Recipe API and Opus Color Palette, utilizing Google Cloud Platform tools like Vertex AI, Stable Diffusion models, and Firestore. Proficient in handling large-scale systems serving millions of users with high efficiency and reliability. - Diverse Technical Skill Set and Tool Proficiency
Skilled in Python, SQL, Google BigQuery, Flask, PyTorch, and Tableau for AI, data analysis, and backend development. Demonstrates expertise in deploying machine learning and deep learning models, automating data pipelines, and integrating AI technologies into business applications. - Notable Projects and Research Contributions
Developed impactful solutions like an Indian Sign Language recognition system using CNNs, achieving 73.5% accuracy, and implemented a TTS model using Coqui with a MOS of 4.2/5. Published research on predicting renewable energy generation, applying advanced machine learning techniques to solve environmental challenges. - Collaborative and Innovative Problem-Solving Approach
Led cross-functional teams in creating AI-powered applications and data-driven tools, contributing to projects with a customer-centric approach. Successfully bridged the gap between technology and business needs, delivering measurable outcomes while fostering innovation and efficiency.