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AI Engineer with 1+ years in Agentic AI and Edge Computer Vision
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Assessing your cultural and operational fit
Machine Learning Engineer actively seeking AI-centric roles to leverage expertise in Agentic AI and edge computer vision. Transitioning to focus strictly on building autonomous LLM reasoning pipelines, high-throughput tracking models, and hardware-optimized deployments (TensorRT/Docker/AWS) that deliver measurable improvements in precision, sub-second latency, and operational efficiency.
IIIT-Delhi
M.Tech · Computer Science and Engineering
August 1, 2023 – June 30, 2025
Netaji Subhash Engineering College
B.Tech · Computer Science and Engineering
N/A – June 30, 2022
Awiros
Computer Vision Engineer
January 1, 2025 – December 31, 2025
Gurgaon, Haryana, India
Aerial Guardian: Edge-Optimized Tracking
June 27, 2026 – Present
Engineered a high-throughput multi-object tracking pipeline fine-tuning a YOLOv8 head on the VisDrone & MOT dataset, integrating BoT-SORT with Global Motion Compensation (GMC) to improve mAP50 from 0.36 to 0.46. Architected an automated edge-deployment suite cross-compiling PyTorch models into TensorRT (INT8/FP16) and ONNX, achieving a peak throughput of 257 FPS (3.8ms latency) utilizing hardware-accelerated FFmpeg NVENC.
View ProjectAI-Powered Legal Assistant
June 27, 2026 – Present
Architected an Agentic RAG pipeline using LangChain for multi-hop reasoning, orchestrating autonomous tool-use across Hybrid Retrieval (FAISS + BM25) to optimize citation accuracy for complex legal corpora. Engineered an autonomous Voice-to-Voice agent utilizing quantized Faster-Whisper, enabling conversational memory and dynamic task execution with sub-second latency and a 40% reduction in reload time.
View ProjectCultural Fit Analysis
The candidate's project diversity, ranging from legal assistants to edge-optimized tracking and eKYC systems, demonstrates adaptability and a broad interest in applying AI across different domains. The focus on 'Agentic AI' and 'edge computer vision' aligns well with cutting-edge AI development, suggesting a fit for innovative and challenging environments. The academic background and teaching assistant experience indicate a collaborative and learning-oriented mindset. The target role of AI Engineer is a strong match for the candidate's stated career goals and demonstrated technical expertise.
Soft Skills & Operational Fit
The candidate's resume highlights strong problem-solving skills through quantifiable improvements in various projects and professional experience. The ability to architect end-to-end systems and optimize for performance indicates a proactive and results-oriented approach. The teaching assistant role suggests good communication and mentorship potential. However, without psychometric or English test scores, a comprehensive assessment of work attitude, stress handling, and team collaboration is not possible.