Sai Naga Lakshmi
Career Objective
AI Engineer with 0 years of experience building end-to-end ML systems for time-series anomaly detection and adaptive learning. Skilled in Python, NumPy and Scikit-learn for model development, and FastAPI and OpenAPI for serving model-driven endpoints. Built a pure-Python anomaly detector combining rolling z-score features with an Isolation Forest ensemble and a synthetic-data generator, improving F1 by 28.6 points versus a fixed-threshold baseline. Designed an adaptive curriculum engine using Bayesian Knowledge Tracing and a prerequisite DAG, served by FastAPI and persisted in PostgreSQL, which reduced attempts-to-mastery by 24% in simulation. Comfortable implementing REST APIs, building React.js front-ends, and managing code with Git/GitHub. Seeking an entry-level AI/ML engineering role focused on productionizing ML models, applied research, and data-driven product features.
Education
Technical Skills
Projects
- Implemented a pure-Python anomaly detector for real-valued time-series, engineering local and global rolling z-score features to capture transient spikes and level shifts using NumPy.
- Trained and integrated an Isolation Forest ensemble with the feature pipeline using Scikit-learn to separate contextual anomalies from noise.
- Developed a synthetic-data generator that injects labeled spike, level-shift, and dropout anomalies to produce ground-truth test sets for robust evaluation.
- Benchmarked model performance against a fixed-threshold baseline and improved F1 by 28.6 points (0.675 vs 0.389) while maintaining 72.3% precision.
- Constructed reproducible evaluation scripts and version-controlled experiments using Git and GitHub to track model variants and datasets.
- Designed and implemented an adaptive curriculum engine implementing Bayesian Knowledge Tracing to estimate per-topic learner mastery in real time.
- Architected a prerequisite-DAG curriculum and a zone-of-proximal-development spaced-repetition scoring policy to prioritize topic recommendations.
- Implemented RESTful endpoints and OpenAPI specifications using FastAPI to serve personalized recommendations and learner-state queries.
- Integrated PostgreSQL for persistent learner-state and progress tracking, designing schemas to efficiently store per-learner per-topic mastery vectors.
- Benchmarked the adaptive engine against a fixed course order on 300 simulated learners and reduced attempts-to-mastery by 24%.
Certifications
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