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Aisha Sartaj
Mid-level AI Engineer specializing in LLM systems, RAG, and MLOps
ILMAscentUCLARemote3 Years ExperienceMid LevelWorks On-Site
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About
Built an LLM multi-agent “ingredient safety” analyzer for cosmetics that cuts consumer research time from ~20+ minutes to minutes, using LangGraph orchestration, hybrid retrieval (Qdrant + Tavily), and safety-focused critic validation (false rejections reduced ~30%→~8%). Also has research-internship experience building computer-vision pipelines to classify emerald color/clarity by translating gem-expert heuristics into quantitative model features.
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Built and deployed a multi-agent cosmetic ingredient safety analyzer with personalized recommendations
Designed agent workflow with supervisor/research/analysis/critic roles and conditional routing in LangGraph
Solved JS-rendered scraping blockers by adding rate limiting/retries and identifying need for Selenium
Improved critic agent performance by refocusing validation on safety-critical issues (reduced false rejections from ~30% to ~8%)
Pragmatic model selection based on cost/latency constraints (Gemini 2.0 Flash for ~20–30s responses)
Implemented hybrid retrieval with Qdrant semantic search plus web-search fallback using similarity threshold (<0.7)
Established reliability approach with measurable criteria (completeness, allergen detection as non-negotiable, latency) and iterative real-case testing
Collaborated with non-technical domain experts to convert qualitative gemstone grading into quantitative CV classification criteria
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Experience
AI EngineerILMAscent · Mar 2025 – Present
Applied CV Research InternInstitute of Applied AI and Robotics (IAAIR) · Jun 2025 – Sep 2025internship
AI Engineer Intern (Capstone Project)Leadoff.ai · Jun 2025 – Aug 2025internship
AI Software Engineer & Data EngineerFractal Analytics · Sep 2022 – Sep 2024
Education
UCLAmaster, Data Science (2025)
Vellore Institute of Technologybachelor, Computer Science and Engineering (2022)
Awards
Overall Best - AWS x Bruin AI Hackathon (MoodFlow)
Mid-Level Software Engineer specializing in Generative AI and RAG systems
Remote, USA5y exp
MetaUniversity of North Carolina at Charlotte
“Built a production RAG-based natural-language-to-SQL system at Global Atlantic to replace slow, expensive manual analytics ticket workflows, focusing heavily on retrieval quality and measurable evaluation (200-question ground-truth set; recall@5 improved 0.65→0.78 via semantic chunking). Also built a custom MCP-style agent orchestrator for a personal project (arxiv-ai) to improve flexibility and Langfuse-aligned observability, and has hands-on experience with LangGraph, CrewAI, and n8n.”
Junior AI Engineer specializing in fraud detection, credit risk, and LLMs in FinTech
Remote, USA3y exp
JPMorgan ChaseUniversity of Illinois Urbana-Champaign
“AI engineer with production experience building a high-accuracy (98%) fraud detection system operating at real-time latency (1–2s) over millions of transactions, using a multi-model pipeline approach to meet performance constraints. Also implemented Airflow-orchestrated workflows (DAGs, retries, alerts) to replace brittle cron scripts and is currently pursuing a master’s project on real-time ASL-to-text conversion.”