Building intelligent systems across AI, ML, software, and research
I’m a graduate student in Artificial Intelligence at Northeastern University with a strong interest in building systems that combine machine learning, intelligent reasoning, and software engineering to solve meaningful real-world problems.
My experience spans AI research, applied machine learning, generative AI, reinforcement learning, and full-stack system development. I’ve worked on projects involving multi-agent systems, NLP, sentence-level language modeling, retrieval-augmented generation, voice-based AI, and reinforcement learning environments.
I’m open to a wide range of roles including AI, ML, software, applied research, data-driven engineering, and intelligent systems development. I enjoy going deep into problems, learning by building, and turning ambitious ideas into practical systems.
- Artificial Intelligence Roles
- Machine Learning Roles
- GenAI Roles
- AI Research and Applied Research Roles
- Software Development Roles
- Intelligent Systems and Product Engineering Roles
Multi-Agent AI Hiring Platform
A full-stack AI platform for resume analysis, candidate evaluation, real-time AI interviews, and career guidance. Built with a multi-agent orchestration framework, retrieval-augmented reasoning, and real-time voice interaction.
Core Areas: Multi-Agent Systems, RAG, LLM Orchestration, Realtime AI, Full-Stack Development
Research project in sentence-level language modeling
Explored next-sentence prediction using sentence-level attention and latent sentence representations instead of conventional next-token prediction.
Core Areas: NLP, Transformers, Representation Learning, Latent Modeling, Research
Multi-Agent Reinforcement Learning Environment
A custom environment modeling adaptive combat behavior using PPO, Genetic Algorithms, and Simulated Annealing, with real-time visualization.
Core Areas: Reinforcement Learning, Optimization, Simulation, Multi-Agent Systems
Public-sector AI feedback system
Built a privacy-first AI platform for voice and text interviews to capture post-training reflections and structured behavioral outcomes.
Core Areas: Conversational AI, Voice Interfaces, NLP, Applied AI, Social Impact
| Area | Skills |
|---|---|
| Languages | Python, Java, R, SQL, Shell |
| AI / ML | ML, DL, RL, NLP, CV, Transformers, Representation Learning, Latent Modeling, Sequence Modeling |
| LLM / Agents | Prompt Engineering, Model Evaluation, Multi-Agent Systems, RAG |
| Frameworks | PyTorch, TensorFlow, Keras, Hugging Face, FastAPI, Flask, LangChain, LangGraph |
| Full Stack / Tools | React, Next.js, OpenCV, Postman, Pygame |
| Data & Infra | MySQL, Snowflake, ChromaDB, FAISS, GCP, Hadoop, Git, CI/CD, Docker |
| Core CS | DSA, System Design, API Dev, Optimization, Feature Engineering, Semantic & Vector Search |
- Built AI-driven systems in industry and academia across NLP, RL, automation, and end-to-end platform development
- Worked on LSTM-based error prediction and NLP-driven automation at Wipro
- Contributed to a Generative AI platform for public-sector impact evaluation at the Burnes Center for Social Change
- Developed projects spanning research, product systems, multi-agent workflows, and AI for social impact
- Intelligent systems that solve real problems
- Applied AI products with strong system design
- Research-driven NLP and language modeling
- Reinforcement learning environments and simulations
- Multi-agent orchestration pipelines
- Retrieval and reasoning systems
- Human-centered AI applications
- Building practical AI systems that are reliable and useful
- Exploring sentence-level language modeling and representation learning
- Designing agentic workflows and retrieval-based systems
- Learning continuously through implementation, experimentation, and iteration
I may not know everything, but I’m deeply curious and committed to learning. When something genuinely interests me, I like to go deep, understand how it works, experiment with it, and build something meaningful from it.