Company Description

Vehikonik.ai is a knowledge platform focused on simplifying the technology behind modern, connected, and software-defined vehicles. As the automotive industry moves toward Connected Cars, SDVs, AI, and smart mobility, Vehikonik.ai creates concise and technically accurate content for engineers, students, automotive professionals, and technology enthusiasts. The platform covers topics such as connected vehicle technologies, telematics, vehicle cybersecurity, AI and machine learning in automotive, ADAS and autonomous driving, V2X communication, and emerging mobility innovations.

Vehikonik.ai aims to make complex vehicle technologies understandable and actionable, helping learners and professionals stay informed about the future of mobility. The mission is to drive knowledge and empower people who are shaping the next generation of automotive technology.

Role Description

We are seeking a motivated Machine Learning & IoT Data Analytics Intern with a strong passion for time-series modeling and smart sensor analytics. In this hands-on role, you will work closely with our engineering team to clean, analyze, and build baseline ML models on high-frequency power and electrical telemetry stream data.

Your core focus will be exploratory data analysis, signal preprocessing, and experimenting with time-series algorithms to help extract unique device load signatures and detect abnormal consumption patterns from connected smart plug hardware.

Key Responsibilities:

- Data Wrangling & Cleaning: Ingest, preprocess, and clean high-frequency telemetry streams (voltage, current, active power, power factor) from connected IoT devices.
- Exploratory Data Analysis (EDA): Perform EDA to identify baseline electrical draw patterns, phantom loads, and distinct appliance signature features.
- Baseline ML Modeling:



Implement and evaluate prototype time-series classification and anomaly detection algorithms (e.g., Random Forests, Autoencoders, Isolation Forests) using Python frameworks.
- Feature Extraction: Experiment with signal-processing techniques (rolling statistics, peak detection, frequency-domain transformations) to improve feature representation for device classification.
- Dashboarding & Visualization: Build interactive data visualizations and diagnostic reports to help summarize energy usage trends and cost-saving insights.

Qualifications and skills:

- Education: Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, or a related technical field.
- Programming: Solid foundation in Python and standard data science libraries (Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn).
- Data Science Fundamentals: Positive understanding of supervised/unsupervised machine learning concepts and time-series analytical methods.
- Sensors/IoT Curiosity: Basic understanding of reading telemetry signals, JSON payloads, or time-series database structures.
- Problem-Solving: Eagerness to work with noisy, real-world sensor data and a desire to learn how ML systems are built for consumer hardware.

Preferred (nice to have) skills:

- Experience with PyTorch or TensorFlow for time-series or sequence modeling.
- Basic knowledge of signal processing concepts (e.g., Fourier transform, wavelet transforms).
- Prior projects involving energy datasets, smart home technology, or IoT telemetry.

What you will gain?

- Direct mentorship from senior engineers and hands-on experience working with real-world IoT telemetry.
- Opportunity to build portfolio projects centered around applied AI, sustainability, and smart hardware.
- Flexible working hours and a stipend commensurate with qualifications.

📌 Connected System Intern (Gurugram)
🏢 Vehikonik.ai
📍 Gurugram