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AI Engineer with 2+ years in Deep Learning, NLP & 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, Hugging Face 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)
June 25, 2026 – April 1, 2026
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 ProjectCognifyAI - AI Code Analysis Platform
June 25, 2026 – January 1, 2026
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
June 25, 2026 – January 1, 2025
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 ProjectSmartRec - Hybrid Recommendation System
June 25, 2026 – January 1, 2026
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 ProjectCultural Fit Analysis
The candidate's diverse project portfolio, including academic research and personal full-stack AI applications, demonstrates a broad interest and self-driven learning. Their experience with various ML/AI frameworks and tools, coupled with an internship at 'Developer Hub Corporation' and contract work at 'Elevvo PathWays', indicates adaptability and a willingness to engage with different technical challenges. The target role of AI Engineer aligns well with their demonstrated skills and interests.
Soft Skills & Operational Fit
The candidate's project descriptions and research involvement suggest strong problem-solving skills, initiative, and a commitment to scientific rigor. Their experience in building end-to-end AI solutions indicates an operational mindset, capable of bridging research with practical deployment. The focus on low-latency inference and CI/CD integration points to an understanding of production requirements.