AI Engineer with 2+ years in Deep Learning & Production API Development
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Junior ML/AI Engineer with hands-on experience in deep learning, NLP, and production API development. Authored independent research on learnable attention normalization in Transformer architectures (NeurIPS 2026 workshop, under review). Proficient in PyTorch, HuggingFace Transformers, LangChain, FastAPI, and MLOps tooling. Focused on bridging research-grade ML with scalable backend systems.
Memon Industrial and Technical Institute (MITI)
Diploma of Associate Engineering · Software Engineering
N/A – June 30, 2026
Developer Hub Corporation
ML/AI Engineer Intern
January 1, 2024 – Present
India
Elevvo PathWays
ML API Intern / ML Specialist (Contract)
January 1, 2024 – Present
India
Adaptive Activation Attention (A3)
April 1, 2026 – Present
Proposed replacing fixed softmax in Transformer self-attention with a learnable, input-conditioned gated transformation that adapts attention distributions per input context. Evaluated with bootstrap confidence intervals and explicit p-value reporting; includes null result analysis for scientific transparency. Submitted to NeurIPS 2026 workshop (Empirical Findings track).
View ProjectSmartRec - Hybrid Recommendation System
January 1, 2026 – Present
Designed a hybrid recommender combining collaborative filtering and content-based signals via a neural matrix factorization model trained in PyTorch on user-item interaction data. Exposed real-time recommendation endpoints through FastAPI with PostgreSQL-backed user and item storage; implemented embedding caching to reduce inference latency.
View ProjectCognifyAI - AI Code Analysis Platform
January 1, 2026 – Present
Built a full-stack AI-assisted code analysis platform with a Monaco Editor frontend and a multi-stage FastAPI backend pipeline: syntax validation → bug detection → security scanning → complexity analysis → optimization → docstring generation. Combined AST-based static analysis with LLM-powered enrichment via LangChain; outputs line-level bug reports, CVE-mapped security findings, time/space complexity estimates, and an overall quality score.
View ProjectFake News Detection
January 1, 2025 – Present
Developed an end-to-end fake news detection web app: fine-tuned a RoBERTa model on a labeled news corpus for binary classification (REAL / FAKE) with confidence scoring. Served the model via a FastAPI inference endpoint and built a React (Vite + TypeScript) frontend for real-time headline/article prediction with probability output.
View ProjectCultural Fit Analysis
The candidate's diverse personal projects (code analysis, fake news detection, recommendation systems) and research initiative demonstrate a strong passion for AI and continuous learning, which aligns well with an innovative culture. Their experience with various ML frameworks and deployment tools indicates adaptability. The current internship roles are directly relevant to an AI Engineer position, showing clear career alignment. However, the candidate is still pursuing a diploma, which might indicate a need for mentorship and structured guidance in a professional setting.
Soft Skills & Operational Fit
The candidate's project descriptions and research involvement suggest strong problem-solving skills, initiative, and a commitment to scientific transparency. Their experience with CI/CD and Docker indicates an understanding of operational best practices for ML systems. However, without direct assessment data, specific soft skills like teamwork or stress handling cannot be definitively evaluated.