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AI Engineer with 1+ years in AI/ML, Python & LangChain
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AI/ML Engineer building production LLM, RAG, and agentic systems, with a live AI product (TryOwn) serving real users and a deep-learning internship in computer vision. Backend experience at Orange Business on blockchain-based cloud storage. Comfortable across Python, LangChain, FastAPI, vector search, multi-agent orchestration, and full-stack development.
Netaji Subhas University of Technology, New Delhi
B.Tech. · Electronics & Internet of Things (IoT)
August 1, 2020 – June 30, 2024
ORANGE BUSINESS
Backend Engineer (Account Associate)
June 1, 2025 – Present
Gurgaon, Haryana, India
EIGENGRAM
AI Engineer Intern
November 1, 2024 – May 1, 2025
India
TRYOWN (Live Virtual Try-On Fashion Platform)
June 22, 2026 – Present
Built and shipped a full-stack AI fashion platform live with real users, featuring virtual try-on, pose transfer, a marketplace, and a community forum. Engineered a recommendation engine using FashionSigLIP 512-d embeddings in pgvector, with interaction-weighted vector blending for cosine-similarity ranked feeds. Served model inference through a FastAPI service; built forum feed-ranking (hot, rising, top, new) with nested comment threading and Redis-based per-user rate limiting.
Autonomous macOS AI Agent
June 22, 2026 – Present
Designed a 7-tier cost-tiered execution engine (shell, URL schemes, AppleScript, Chrome JS, Accessibility API, vision-LLM, GUI), auto-routing tasks to the cheapest working route and cutting average token cost ~10x vs. a pure vision loop. Built context engineering for long sessions: a 3-pass history curator (screenshot collapse, failure deduplication, LLM compaction) keeping the context window clean across 14-iteration runs; episodic memory with Bayesian-smoothed per-tier priors and fcntl.flock safe concurrent writes. Built a harness-based evaluation system: frozen regression corpus of 10 cases, keyword + LLM-as-judge scoring, predicate-based offline evaluation, and pytest integration for CI. Designed a multi-agent orchestration layer with 3 topologies (N-way debate; Planner → Researcher → Executor → Verifier pipeline; parallel fan-out), a Planner-Critic-Judge flow that adversarially reviews plans before any side-effecting action, plus a shared blackboard, asyncio message bus, and resource locks on keyboard/mouse/chrome_tab.
View ProjectPDF RAG Application
June 22, 2026 – Present
Built a retrieval-augmented generation app for Q&A over PDFs, docs, and URLs using semantic chunking and FAISS vector search. Designed prompt templates and a retrieval-evaluation loop to reduce off-topic and hallucinated answers.
OCR Text Extraction Tool
June 22, 2026 – Present
Built a batch document pipeline with preprocessing (deskew, denoise, contrast enhancement) to improve extraction on noisy scans, automating conversion to structured JSON with per-field confidence scores, tested across receipts to academic papers.
Cultural Fit Analysis
The candidate's project portfolio showcases a strong passion for AI/ML, with a focus on building practical, real-world applications. The diversity of projects, from fashion platforms to macOS agents and RAG applications, indicates a broad interest and ability to apply AI across different domains. The experience with both backend engineering and AI/ML roles aligns well with an AI Engineer position, suggesting a good fit for roles requiring both model development and system integration. The competitive programming background also points to a culture of continuous learning and problem-solving.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and problem-solving skills through their diverse personal projects, many of which are live or have detailed architectural descriptions. Their ability to build full-stack applications and complex AI agents suggests a proactive and independent work attitude. The detailed descriptions of evaluation systems and cost optimization in projects indicate a methodical and performance-oriented approach. The candidate's experience with both internships and personal projects suggests an ability to work in structured and self-directed environments.