AI/ML Engineer | Computer Vision • LLMs • RAG • MLOps | Building scalable AI systems with PyTorch, YOLO, FastAPI & AWS.
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Novus Hi-Tech
Data Scientist
June 16, 2026 – Present
Terrain-Traversability-Segmentation-for-Autonomous-Ground-Robots
May 31, 2026 – Present
This project presents a real-time semantic terrain segmentation framework capable of identifying 19 terrain and environmental classes across diverse operating conditions.
View ProjectHybrid-Physics-ML-Wind-Farm-Layout-Optimization
May 26, 2026 – Present
Hybrid physics–ML system to optimize wind turbine placement and maximize energy output using Jensen wake modeling, SLSQP optimization, and XGBoost surrogate modeling.
View ProjectAutoValuator-AI-LLM-Powered-Car-Price-Prediction-Assistant
May 23, 2026 – Present
Production ML system: RandomForest + ColumnTransformer pipeline (scikit-learn) served via FastAPI, with a Groq/Gemini/Ollama LLM layer for natural language feature extraction and price explanation. Multi-turn conversation memory via Redis, JWT + API key auth, Prometheus metrics, Grafana dashboards, and a Streamlit chat UI — fully containerized with
View ProjectRoad_segmentation_for_autonomous_vehicles
May 23, 2026 – Present
Deep learning image segmentation pipeline using UNet, YOLOv8, and Detectron2 on custom road datasets for autonomous driving applications.
View ProjectPerception-and-Navigation-for-Mobile-Robots-and-UAVs
July 10, 2025 – July 10, 2025
This Repository is focused on sensor fusion,mapping,localisation, SLAM and PLanning for Mobile RObots.
View ProjectObstacle-Avoidance-Path-Planning-Static-Environment
June 8, 2024 – June 8, 2024
Obstacle Avoidance Path Planning in a Static Environment involves training an RL agent, such as a robot or autonomous vehicle, to navigate a fixed environment while avoiding collisions with static obstacles.
View ProjectObstacle_Avoidance_Path_Planning_Static_Environment
November 25, 2023 – January 20, 2024
Obstacle Avoidance Path Planning in a Static Environment involves training an RL agent, such as a robot or autonomous vehicle, to navigate a fixed environment while avoiding collisions with static obstacles.
View ProjectCultural Fit Analysis
The candidate's project portfolio shows a strong inclination towards individual, technically challenging projects, particularly in robotics and AI. While this demonstrates initiative and deep technical interest, the lack of team-based projects or explicit collaboration details makes it difficult to assess cultural fit for a collaborative team environment. The projects are diverse within the AI/ML domain, but the overall breadth of experience outside of this specific technical niche is limited. The candidate's experience level is listed as 0, which contradicts the current full-time role as a Data Scientist starting in 2026, suggesting a data inconsistency or future role. Assuming the projects represent their current capabilities, the fit is strong for a technically driven role, but team integration aspects are unknown.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a strong technical focus and ability to work on complex problems, but collaboration, communication, and problem-solving approaches in a team setting cannot be evaluated without interview data or specific assessments.