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AI & Data Science graduate with an end-to-end focus on building production-ready AI/ML, NLP, and computer vision pipelines from raw data to deployed applications. Strong in Python-based engineering, model evaluation, and systems design to improve latency and reliability in real-world use cases. Experienced in developing predictive analytics for vehicle/industrial sensor maintenance and building efficient AI pipelines for face/missing-person identification using OpenCV feature extraction, efficient storage/indexing, and similarity search with measurable accuracy and speed improvements.…AI & Data Science graduate with an end-to-end focus on building production-ready AI/ML, NLP, and computer vision pipelines from raw data to deployed applications. Strong in Python-based engineering, model evaluation, and systems design to improve latency and reliability in real-world use cases. Experienced in developing predictive analytics for vehicle/industrial sensor maintenance and building efficient AI pipelines for face/missing-person identification using OpenCV feature extraction, efficient storage/indexing, and similarity search with measurable accuracy and speed improvements.

Binsika

AI Engineer, Data Scientist, Data Engineer, +1

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AI & Data Science graduate with an end-to-end focus on building production-ready AI/ML, NLP, and computer vision pipelines from raw data to deployed applications. Strong in Python-based engineering, model evaluation, and systems design to improve latency and reliability in real-world use cases. Experienced in developing predictive analytics for vehicle/industrial sensor maintenance and building efficient AI pipelines for face/missing-person identification using OpenCV feature extraction, efficient storage/indexing, and similarity search with measurable accuracy and speed improvements.…AI & Data Science graduate with an end-to-end focus on building production-ready AI/ML, NLP, and computer vision pipelines from raw data to deployed applications. Strong in Python-based engineering, model evaluation, and systems design to improve latency and reliability in real-world use cases. Experienced in developing predictive analytics for vehicle/industrial sensor maintenance and building efficient AI pipelines for face/missing-person identification using OpenCV feature extraction, efficient storage/indexing, and similarity search with measurable accuracy and speed improvements.

Available to hire

AI & Data Science graduate with an end-to-end focus on building production-ready AI/ML, NLP, and computer vision pipelines from raw data to deployed applications. Strong in Python-based engineering, model evaluation, and systems design to improve latency and reliability in real-world use cases.

Experienced in developing predictive analytics for vehicle/industrial sensor maintenance and building efficient AI pipelines for face/missing-person identification using OpenCV feature extraction, efficient storage/indexing, and similarity search with measurable accuracy and speed improvements.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

Tamil

Fluent

English

Advanced

Malayalam

Intermediate

Work Experience

AI/ML Engineer at Infosys SpringBoard | Remote

February 1, 2026 - March 1, 2026

Built an end-to-end AI missing-person identification system. Implemented a modular pipeline using OpenCV for feature extraction, SQLite BLOB embedding storage with composite indexing, and cosine-similarity search. Optimized query latency to under 15 seconds and achieved 70–80% precision on 50–100 face gallery entries via a modular Flask interface. Developed a Python tool to parse source code into an AST and dump into a custom JSON format for LLM checks to catch bad patterns and logic bugs. Split the system into modular Flask services to cache responses and reduce analysis time from ~8 seconds to under 3 seconds. Also engineered a predictive maintenance system for factory vehicle sensors to detect failing parts before breakdown. Designed custom physics-inspired features (e.g., heat deltas and tool wear ratios) and trained models (Random Forest and XGBoost) with comparisons on heavily skewed data to find approaches that identify real failures without spamming false alerts.