Aaditya Srinivasan

Product Manager

Ghaziabad, Uttar Pradesh, India0 mo experience

AI EnabledAI ML Practitioner

Key Highlights

Passionate about building efficient software systems.
Experience in backend engineering and AI/ML internships.
Strong foundation in algorithms and competitive programming.

Stackforce AI infers this person is a Backend Engineer with a focus on AI/ML and competitive programming.

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Skills

Core Skills

Computer VisionAi/ml

Other Skills

Convolutional Neural Networks (CNN)Data PreparationData CleaningDashboardsC++APIBig DataData ScienceHadoopMapReduceHiveJupyterPandasFirebaseReact Native

About

Computer Science student passionate about building efficient software systems and improving problem-solving skills through algorithms and competitive programming. I enjoy working close to the fundamentals of software engineering — designing efficient data structures, optimizing code, and building backend systems that scale. My experience includes developing backend services using Java Spring Boot and deploying applications on cloud infrastructure. I have also worked on computer vision systems during my AI/ML internship, where I helped build a real-time safety monitoring pipeline. Currently focused on: • Competitive programming • Advanced data structures and algorithms • Systems programming in C++ • Backend engineering and distributed systems

Experience

0 mo

Total Experience

Average Tenure

Current Experience

K6

Open Source Contributor

Mar 2026 – Present · 2 mos

Contributed to Grafana k6 load testing tool by improving CLI help output (merged PR)
Collaborated with maintainers to refine UX and pass CI/lint checks

Aegis

Developer Lead

Aug 2025 – Mar 2026 · 7 mos · Ghaziabad, Uttar Pradesh, India · On-site

Infosys springboard

AI & Machine Learning Intern

Aug 2025 – Oct 2025 · 2 mos · Remote

Engineered and optimized core components of SafetyEye, an AI-powered workplace safety system used to detect PPE compliance in real time.
Reduced model-to-alert latency from 5s → 2s, improving system responsiveness by 60% and significantly enhancing on-site safety decision-making.
Integrated ML models into production-like Flask backend workflows, improving alert reliability and deployment stability across multiple edge cases.
Collaborated with ML and product teams to refine detection logic, contributing to 87% mAP@0.5 performance and higher alert accuracy.
Developed REST APIs and streamlined backend pipelines, leading to smoother communication between model endpoints and dashboard systems.
Designed dashboard-level UX improvements that helped convert raw detection outputs into actionable insights and heatmaps, boosting operator clarity and reducing review time.
Helped define MVP priorities and telemetry requirements, enabling better tracking of model performance and real-world system behavior.

Computer VisionConvolutional Neural Networks (CNN)AI/ML