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AI Research Engineer with 1+ years in Computer Vision, NLP & Signal Processing
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Assessing your cultural and operational fit
Highly motivated AI/ML Research Engineer pursuing a Master's in Artificial Intelligence with 1.8 years of hands-on experience in developing advanced computer vision and signal processing pipelines. Proven ability to deliver high-accuracy solutions for complex problems across diverse modalities, optimize data processing, and lead impactful projects in areas like gait analysis, AVM detection, and crop disease classification. Proficient in PyTorch, TensorFlow, Python, and cloud platforms, with a strong foundation in deep learning, NLP, and MLOps.
Stony Brook University
Master of Science · Artificial Intelligence
August 1, 2024 – May 1, 2026
Savitribai Phule Pune University
Bachelor of Engineering · Artificial Intelligence and Data Science
August 1, 2020 – May 1, 2024
Stony Brook Medicine
Research Assistant - Machine Learning
May 1, 2025 – Present
Stony Brook, New York, United States
RWTH Aachen University – Pune Office
AI/ML Research Intern
January 1, 2024 – April 1, 2024
Pune, Maharashtra, India
Automaton AI
Data Science Intern
January 1, 2023 – March 1, 2023
Pune, Maharashtra, India
CourseBot - Domain-Adapted LLM
June 16, 2026 – Present
Boosted domain MCQ accuracy by 22.2% (55.6% → 77.8%) via transfer learning on generative LLM Mistral-7B using LoRA (rank 64) + 4-bit NF4 quantization; tuned hyperparameters and optimized model training to run under 16GB GPU VRAM. Generated 400 NLP training pairs and embeddings from raw PDFs via Claude API; applied batch gradient accumulation and early stopping to fit the full backpropagation training pipeline within memory budget.
View ProjectTrafficient - Adaptive Traffic Control
June 16, 2026 – Present
Reduced average vehicle wait time by 54% over a fixed-timing baseline by training a DQN agent for real-time adaptive signal control in SUMO simulation. Improved network-level throughput by 34.9% by extending to a multi-agent setting with distributed policy learning and attention-based state encoding across intersections.
View ProjectHardware Accelerator for 2D Convolution
June 16, 2026 – Present
Synthesized a pipelined MAC unit at 1.25 GHz (44% faster than unpipelined baseline) with FSM datapath and AXI-Stream interface; boosted throughput a further 16% by applying double-buffering to overlap memory I/O with compute. Profiled area-delay-power trade-offs across design parameter sweeps; produced optimization recommendations for low-latency inference on edge-targeted FPGA hardware.
Cultural Fit Analysis
The candidate's academic projects and internships demonstrate a strong interest in cutting-edge AI research and practical application. The range of projects, from LLM adaptation to adaptive traffic control and hardware acceleration, shows intellectual curiosity and a willingness to explore diverse domains within AI. The current research assistant role at Stony Brook Medicine further highlights a commitment to impactful, real-world AI applications. This aligns well with a research-focused role that values innovation and problem-solving.
Soft Skills & Operational Fit
The candidate's project and work experience descriptions indicate strong problem-solving abilities, a results-oriented approach (quantified achievements), and an aptitude for optimizing solutions under constraints. The diversity of projects suggests adaptability and a proactive learning attitude. While direct evidence of teamwork or communication in a corporate setting is limited, the detailed project descriptions imply effective technical communication.