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AI Engineer with 1+ years in Deep Learning & Generative AI
Results-driven AI & Machine Learning Engineer with hands-on experience in developing intelligent applications using Python, Machine Learning, Deep Learning, and Generative AI technologies. Skilled in building LLM-powered solutions, Retrieval-Augmented Generation (RAG) pipelines, NLP systems, semantic search applications, and predictive analytics models using PyTorch, TensorFlow, Scikit-learn, and Hugging Face. Experienced in data preprocessing, feature engineering, model evaluation, backend API integration, and deploying AI-driven workflows for real-world applications. Strong foundation in data structures, algorithms, software engineering, and problem-solving, with a passion for leveraging AI to build scalable, impactful, and innovative solutions.
Amrita Vishwa Vidyapeetham, India
M.Tech in Computer Science and Engineering · Artificial Intelligence and Machine Learning
August 1, 2022 – June 30, 2024
Amrita Vishwa Vidyapeetham, India
Bachelor of Technology · Electrical and Electronics Engineering
August 1, 2017 – June 30, 2021
Right Soft Options
AI/ML Project Developer
January 1, 2026 – April 1, 2026
Cochin, Kerala, India
Zecser Business LLP
AI Developer
September 1, 2025 – December 1, 2025
Cochin, Kerala, India
Amrita Vishwa Vidyapeetham
Teaching Assistant / AI-IoT Lab In-Charge
January 1, 2022 – October 1, 2022
Thiruvananthapuram, Kerala, India
AI Utility Tool using Gemini LLM: Text & Article Summarizer
January 1, 2025 – June 1, 2025
Built an LLM-powered summarization system using Gemini 1.5 Flash, enabling efficient processing of long documents and PDFs. Implemented configurable inference controls and robust document pipelines, reducing manual review time by 60.
View ProjectAI PDF Content Query Application
January 1, 2025 – June 1, 2025
Developed a NLP-based QA system using DistilBERT and embeddings, enabling context-aware retrieval without external API dependency. Optimized chunking and semantic retrieval for large documents (100+ pages), achieving 85% accuracy..
View ProjectFetal Heart Ultrasound Image Enhancement via GAN and Grad-CAM
January 1, 2024 – June 1, 2024
Applied Pix2Pix GAN models to enhance fetal heart ultrasound images, producing 98.9% segmentation accuracy and improving diagnostic consistency. Leveraged PCA and K-means clustering to uncover anatomical patterns from unlabeled imaging data, accelerating model training efficiency by approximately 30%. Employed Grad-CAM visualization techniques to identify critical prediction regions, reinforcing clinical interpretability and model transparency.
Algorithms on Graphs
Coursera
June 1, 2026 – Present
Continuous Integration and Continuous Delivery (CI/CD)
IBM - Coursera
June 1, 2026 – Present
AWS Cloud Technical Essentials
Coursera
June 1, 2026 – Present
Coding Essentials - Logic Building
Udemy
June 1, 2026 – Present
MLOps-Machine Learning Operations Specialization
Coursera
June 1, 2026 – Present
Django Application Development with SQL and Databases
Coursera
June 1, 2026 – Present
Artificial Intelligence Foundations: Machine Learning
LinkedIn Learning
June 1, 2026 – Present
Introduction to Containers w/ Docker, Kubernetes & OpenShift
Coursera
June 1, 2026 – Present
Introduction to Cloud Computing
Coursera
June 1, 2026 – Present
Introduction To Python Programming
Udemy
June 1, 2026 – Present
IBM RAG and Agentic AI Professional Certificate
Coursera
June 1, 2026 – Present
Learn to Program : The Fundamentals
University of Toronto - Coursera
June 1, 2026 – Present
Hands-on Internet of Things
Coursera
June 1, 2026 – Present
Intro to TensorFlow for Deep Learning
udacity
June 1, 2026 – Present
Computer Science : Programming with a Purpose
Princeton University - Coursera
June 1, 2026 – Present
Fetal Heart Ultrasound Image Enhancement and Anatomical Feature Recognition via GAN and Grad CAM
IEEE
January 1, 2024 – Present
Smart Home with condition monitoring
Springer
January 1, 2021 – Present
Cultural Fit Analysis
The candidate's project diversity, including LLM summarization, NLP-based QA, and medical image enhancement using GANs, demonstrates a broad interest and capability in various AI applications. The experience as a Teaching Assistant and AI/ML Project Developer indicates a collaborative and mentorship-oriented approach, which could be a good cultural fit for teams valuing knowledge sharing. The certifications in MLOps, CI/CD, and cloud computing show a proactive approach to learning and staying current with industry practices, aligning with a growth-oriented culture. However, the psychometric test score is low, which might indicate potential challenges in certain aspects of cultural fit related to work attitude or stress handling, requiring further investigation.
Soft Skills & Operational Fit
The candidate's resume highlights soft skills such as team collaboration, communication, time management, agile mindset, self-motivation, analytical thinking, and adaptability. The psychometric test score of 171/500 suggests potential areas for development in logical reasoning, work attitude, stress handling, or team collaboration, which could impact operational fit. However, the project descriptions and work experience indicate a capacity for structured problem-solving and project execution.