Tarun Srivastava

AI Engineer

https://talent.gravityer.com/tarun-srivastava

AI Engineer with 1+ years in data analysis and research.

Innodata, Noida

Key Strengths

Strong academic background in AI/ML with a focus on practical applications.
Demonstrated ability to develop and deploy ML models for specific problems (e.g., intrusion detection, digit recognition).
Experience with Generative AI tools (OpenAI API, LangChain, Prompt Engineering) is highly relevant for an AI Engineer role.
Proficiency in Python and relevant ML libraries (NumPy, Pandas, Scikit-learn, OpenCV).
Published research findings indicate a proactive and research-oriented mindset.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's academic projects demonstrate a strong interest in applying AI/ML to various domains including cybersecurity, IoT, and data analytics. Participation in conferences, workshops, and hackathons, along with multiple publications, indicates a proactive and collaborative approach to learning and problem-solving. The diverse range of projects and internships suggests adaptability and a willingness to explore different technical areas, which aligns well with a dynamic AI engineering environment. The focus on research and innovation also points to a good cultural fit for a role that requires continuous learning and contribution.

Soft Skills & Operational Fit

The candidate's resume highlights soft skills such as Leadership, Networking, Team Work, and Adaptiveness. Project descriptions suggest an ability to work on diverse technical challenges and collaborate (e.g., conference publications, team finalist achievement). The AI Data Specialist role indicates attention to detail and quality assurance, which are valuable for operational fit in AI model development and deployment.

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Projects

Smart Mirror using Raspberry Pi

June 1, 2026 – Present

Developed a Raspberry Pi-powered Smart Mirror integrating real-time weather, news, and calendar data via APIs. Presented at a National Conference and published in the conference proceedings.

Securing Network With Machine Learning

June 1, 2026 – Present

Developed an ML-based intrusion detection system to identify ARP Spoofing, MAC Flooding, and DNS Poisoning attacks in LAN environments. Built an automated threat analysis pipeline using network traffic features and anomaly detection techniques. Published research findings at the 4th International Conference on Emerging Applications of Artificial Intelligence, Machine Learning and Cybersecurity (ICAMC 2026).

Handwritten Digit Recognition Using CNN

June 1, 2026 – Present

Developed a CNN-based handwritten digit recognition system using the MNIST dataset, achieving 98.9% validation accuracy. Built a real-time GUI interface for digit input, prediction, and confidence score generation using deep learning techniques. Published research findings at the Human-Centric & Responsible AI (HCRAI 2026) Conference.

An Analytical Study of Machine Learning Algorithms and Their Future Research Scope

June 1, 2026 – Present

Conducted a comparative analysis of multiple machine learning algorithms including SVM, Random Forest, Gradient Boosting, and MLP models. Evaluated model performance using accuracy, precision, recall, and F1-score metrics on benchmark datasets. Published research findings at the Human-Centric & Responsible AI (HCRAI 2026) Conference.

Smart Mirror

December 1, 2024 – March 1, 2025

Built an interactive smart mirror using Raspberry Pi and Tkinter to display calendar, weather, and news updates. Integrated ultrasonic sensor-based human recognition for touch-free, energy-efficient activation.

AutoMatic Mopping Robot

December 1, 2024 – March 1, 2025

Enhanced and upgraded an IoT-based robotic floor cleaner with dual operation modes with autonomous and manual operation modes. Implemented ultrasonic sensor arrays to enable smart navigation and obstacle avoidance.

Securing Network With Machine Learning

December 1, 2024 – March 1, 2025

Simulated local network attacks (ARP spoofing, MAC flooding, DNS poisoning) in a controlled LAN environment. Improved real-time detection and systematized mitigation system using Python and ML-based models, Digital Evidence Collection.

AcadLib

December 1, 2024 – March 1, 2025

Developed an AI-powered academic intelligence platform that converts university result PDFs into a structured SQLite database through automated extraction and validation pipelines. Built a natural language query system using LangChain and OpenAI APIs, enabling conversational analytics, SQL generation, and automated report creation. Implemented data analytics, confidence scoring, and export functionalities (Excel, CSV, PDF) for generating actionable insights from large academic datasets. Tech Stack: Python, SQL, SQLite, LangChain, OpenAI API, NLP, Prompt Engineering, Data Analytics, PDFPlumber, OpenPyXL, Tkinter.

Optimizing DNS-based DDoS Attack Detection

January 1, 2022 – Present

Developed a lightweight DNS-based DDoS detection model using entropy and time-series features for SOC environments. Built an automated feature engineering pipeline leveraging query ratios, anomalies, and packet size patterns. Achieved 92% detection accuracy with 15% fewer false positives on CIC-DDoS2019 and custom datasets.