AI Engineer with less than a year in Machine Learning & Deep Learning
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Assessing your cultural and operational fit
Highly motivated and results-driven individual pursuing a B.Tech in Computer Science & Engineering with a specialization in AI & Machine Learning. Proficient in Python, C, C++, and various AI/ML frameworks including Transformers and PyTorch. Demonstrated ability to design and implement AI-powered solutions, such as a style-conditioned melody orchestration transformer, a semantic search recommendation system, and an AI-powered color grading assistant, leveraging deep learning and natural language processing techniques.
B.V. Raju Institute of Technology (BVRIT)
B.Tech · Computer Science & Engineering(Artificial Intelligence & Machine Learning)
August 1, 2024 – June 30, 2028
DIxel AI-Powered DaVinci Resolve Color Grading Assistant
January 1, 2026 – Present
Developed at HACKFINITI 2026 (Team House Stark): an LLM-powered assistant that generates professional 6-node DaVinci Resolve color-grading pipelines from natural-language inputs (footage type, scene, mood) using the Anthropic Claude API. Rendered structured JSON grading configurations as a dark-themed single-page web app with one-click Python Resolve script export, demonstrating full AI-to-NLE workflow automation.
SHL Assessment Recommendation System
January 1, 2025 – Present
Built an AI-powered semantic search engine that maps free-text job descriptions to the most relevant SHL psychometric assessments using dense retrieval. Scraped and structured 377+ assessments; generated 384-dimensional MiniLM embeddings and ranked results via cosine similarity, achieving Mean Recall@10 = 0.78. Deployed as a production-ready REST API via FastAPI with sub-500 ms end-to-end latency; designed for direct integration into enterprise HR tooling.
View ProjectMelOrchPro Lite: Style Conditioned Melody to Orchestration Transformer
January 1, 2025 – Present
Designed a full Transformer encoder-decoder that converts symbolic MIDI melodies into 4-genre, multi-instrument orchestral MIDI (Classical, Romantic, Cinematic, Contemporary) without rule-based post-processing. Engineered a 256-token vocabulary (NOTE / DUR / STYLE / INST) with STYLE-token prepending for genre-aware generation; curated a hybrid corpus of ~17,000 MIDI files and applied pitch/tempo augmentation to produce 95,000+ training sequences. Reduced cross-entropy training loss from 2.05 → 0.11 over 15 epochs using FP16 mixed-precision training and gradient accumulation; achieved 0.3-0.6 s inference latency enabling near-real-time orchestration on an RTX 4050.
View ProjectOracle Certified Generative AI Professional
Oracle
January 1, 2024 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from recommendation systems to creative AI tools (music orchestration, video grading), indicates a broad interest and adaptability, which aligns well with an innovative culture. Their involvement in hackathons and academic projects suggests a proactive and learning-oriented mindset. The target role of 'AI Engineer' is well-aligned with their demonstrated skills and project focus. However, the lack of professional experience means cultural fit is primarily assessed on academic and project engagement.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations. Their participation in hackathons suggests a collaborative spirit and ability to work under pressure. The detailed project descriptions indicate good communication of technical concepts. However, without direct work experience, operational fit and stress handling are inferred from academic projects and psychometric test results, which show a moderate score.