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AI Engineer with less than a year in full-stack development and machine learning.
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Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
An empathetic learner and research-driven AI enthusiast, I am passionate about leveraging technology for social good. With 10 months of experience in full-stack development, machine learning, and scalable systems, I bring strong skills in Python, JavaScript, ReactJS, FastAPI, and PyTorch. My project work includes building intelligent platforms and optimizing performance, reflecting my commitment to innovative problem-solving and reliable system architecture.
JK Lakshmipat University
Bachelor of Technology · Computer Science and Engineering
August 1, 2023 – May 1, 2027
Bhabha Public School
12th Grade (CBSE)
N/A – May 1, 2023
Infosys Springboard
AI Intern
February 1, 2026 – March 1, 2026
Bengaluru, Karnataka, India
JK Lakshmipat University
Teaching Assistant
January 1, 2026 – April 1, 2026
Jaipur, Rajasthan, India
Impulsive Web
AI Intern
May 1, 2025 – August 1, 2025
Noida, Uttar Pradesh, India
remember.ai Scalable Long-Term Memory for LLMs
June 23, 2026 – Present
Architected a hybrid memory retrieval system combining semantic similarity, recency, and confidence scoring over PostgreSQL + pgvector (IVFFLAT) with Redis caching, achieving ~20 ms latency for 1,000+ memories at O(log n) scale. Implemented async memory extraction and selective prompt injection to prevent context bloat while preserving high personalization accuracy across long-horizon interactions.
HoneyShield — AI Agentic Honeypot for Scam Intelligence
June 23, 2026 – Present
Designed a multi-layer scam detection pipeline (keywords → regex → NLP) and a Gemini-powered agent ("Alex") to autonomously engage scammers and extract actionable intelligence – UPI IDs, bank details, and phishing links. Engineered an async FastAPI backend with session-based memory and delayed intelligence aggregation, ensuring real-time scammer interaction with resilient fallback handling.
Multi-Label Chest X-Ray Disease Classification Using Deep Learning
June 23, 2026 – Present
Built a multi-label classification system using DenseNet-169, ResNet-50, and EfficientNet-B0 with ImageNet-pretrained weights, custom data loaders, and binary cross-entropy loss to detect 14 thoracic diseases from chest X-rays. Conducted comparative model evaluation across validation loss, accuracy, and ROC-AUC metrics to identify the most clinically effective architecture for multi-label medical imaging.
Cultural Fit Analysis
The candidate's project portfolio is diverse, covering LLM memory, medical imaging, and cybersecurity (honeypot), showcasing a broad interest in AI applications. The involvement in hackathons and research, along with a scholarship, indicates a strong drive for learning and achievement. The 'tech for social good advocate' self-description aligns with a culture that values impact. The experience as a teaching assistant suggests a collaborative and supportive nature. Overall, the candidate appears to be a good cultural fit for a role requiring innovation, continuous learning, and collaboration.
Soft Skills & Operational Fit
The candidate's resume highlights adaptability and time management as soft skills. Project descriptions suggest a problem-solving mindset and the ability to work in agile teams. The teaching assistant role indicates mentoring and organizational skills. The research and hackathon achievements demonstrate initiative and a drive for innovation, which aligns well with a dynamic AI engineering environment.