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AI Engineer with 1+ years in Agentic AI, Machine Learning & Cloud Platforms
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AI Engineer with a Master's from NYU, specializing in Agentic AI systems, Deep Learning, and MLOps. I have successfully shipped production-grade AI solutions for enterprise clients, developed multi-agent and voice-agent systems, and launched financial wellness copilots. My expertise spans architecture, retrieval design, evaluation, deployment, and optimization of AI models for performance and cost efficiency.
New York University (NYU)
Master of Science- MS · Computer Engineering
September 1, 2024 – May 1, 2026
M.S.Ramaiah Institute of Technology
Bachelor of Engineering- BE · Computer Science and Engineering
December 1, 2020 – June 1, 2024
Blood & Treasure
AI Engineer Intern
January 1, 2026 – May 1, 2026
New York City, New York, United States
Incluya
Founding AI Engineer
May 1, 2025 – August 1, 2025
New York City, New York, United States
INDIAN SPACE RESEARCH ORGANISATION (ISRO)
Machine Learning Intern
January 1, 2024 – June 1, 2024
Bengaluru, Karnataka, India
FORTINET
SDE Intern
June 1, 2023 – August 1, 2023
Bengaluru, Karnataka, India
Fairness-Aware Compression for Vision Models
June 1, 2026 – Present
Researched fairness in face-attribute classification on FairFace (7 racial/ethnic groups) and UTKFace (age, gender, race) using ViT, Swin, and ResNet; produced per-demographic accuracy/error profiles and bias metrics to inform mitigation. Implemented INT8 post-training quantization (ONNX Runtime, Intel Neural Compressor) with balanced calibration and selective layer treatment, yielding 4x smaller models and 2-4× faster inference.
Agentic Multimodal Disaster Response for Triage Routing and Assessment
June 1, 2026 – Present
Implemented a LangGraph multi-agent disaster response copilot that ingests alerts, satellite/UAV imagery, and citizen reports; agents handle geospatial reasoning, damage assessment, logistics planning, and multilingual comms with shared memory over SOPs. Human-in-the-loop triage and routing that clusters/deduplicates requests, prioritizes by severity and access, generates routes/manifests and incident briefs; includes PII redaction, policy guardrails, and offline-first fallback.
Hybrid Outlier Smoothing Technique for Efficient LLM Quantization
June 1, 2026 – Present
Designed a hybrid quantization framework using layer-wise sensitivity analysis to dynamically assign SmoothQuant, QuaRot, or SpinQuant per transformer submodule, enabling precise INT4/INT8 optimization. Achieved 74% reduction in model size and 64% decrease in inference latency, while maintaining accuracy within +1.7% perplexity of the baseline model.
Cultural Fit Analysis
The candidate's project diversity, ranging from fairness-aware AI to disaster response and financial wellness copilots, demonstrates a broad interest in applying AI to various domains. Their experience as a Founding AI Engineer at a startup and an AI Engineer Intern at Blood & Treasure indicates an entrepreneurial spirit and ability to thrive in dynamic environments. The academic background and publications suggest a strong inclination towards research and development, which is a good fit for an AI Engineer role that often involves exploring new techniques. The breadth of technical skills listed (Python, PyTorch, LangGraph, Docker, Kubernetes, AWS, Azure) further supports their adaptability and potential to integrate into diverse technical teams.
Soft Skills & Operational Fit
The candidate's project descriptions highlight strong problem-solving abilities, particularly in optimizing AI models and designing complex agentic systems. Their experience in launching a private-beta product and hardening systems for production suggests a results-oriented and responsible approach. The academic projects and publications indicate a proactive and research-driven mindset, which aligns well with an AI Engineer role requiring continuous learning and innovation. The diverse project types (academic, startup, internship) suggest adaptability and a collaborative spirit.