Hi, I'm
Aritra Das
Problem solver — I bend ideas into things that work.
Move your cursor — it's a black hole.
About
Applied Scientist Intern at Microsoft and M.Tech Computer Science student at
IIT Roorkee, specializing in large language models, deep learning, and applied
ML research. Hands-on experience in LLM fine-tuning, agentic systems, model
compression, and interpretability — delivering measurable, reproducible results.
Targeting Applied Scientist, Research Scientist, and AI Engineer roles.
Experience
Applied Scientist Intern — Microsoft
May 2026 – Present · Hyderabad, India
- Designing and building scalable evaluation frameworks to measure the quality, reliability, and safety of LLMs across diverse, real-world tasks.
- Developing automated benchmarking and metric pipelines that surface model failure modes and behavioral regressions for data-driven decisions.
- Collaborating with cross-functional research and engineering teams to turn evaluation insights into model robustness and trustworthiness gains at scale.
Deep Learning Research Intern — TCS Research & Innovation
May 2022 – Jul 2022 · Kolkata, India
- Applied Lottery Ticket Hypothesis pruning to compress VGG, ResNet, and FCN models, retaining 88% accuracy via confidence-driven selective classification.
- Built an ECG time-series classification pipeline, improving cardiac-anomaly detection by 18% over baselines.
- Developed scalable CNN training workflows, cutting training time by 10% and improving reproducibility.
Earlier: Toppr — Mobile App Developer Intern (4+ React Native
quiz modules, 100K+ users); iLRNU — Full-Stack Developer Intern
(React.js, Node.js, MongoDB).
Education
M.Tech, Computer Science & Engineering — IIT Roorkee
2025 – Present · GPA 8.7/10
Coursework: Machine Learning, Deep Learning, Algorithms, Quantum Computing.
B.Engg, Computer Science & Engineering — Jadavpur University
2019 – 2023 · GPA 8.03/10
Coursework: Artificial Intelligence, DBMS, Data Structures & Algorithms.
Projects
Vision Transformers for High-Energy Physics
TensorFlow · Keras
A ViT classifying electrons vs. photons from CERN calorimeter data — 88% accuracy, 0.91 ROC-AUC, beating CNN baselines by 10–12%, with custom patch encoding and attention layers.
Agentic LLM Fine-tuning with RLHF
PyTorch · Transformers
End-to-end RLHF (reward modeling + PPO) on a 1.3B-parameter LLM. Lifted human-preference win-rate 52→68%, cut unsafe actions ~35%, and raised multi-tool task success 61→79%.
Agentic Workflows with kgraph + MCP
Python · Model Context Protocol
A suite of specialized LLM agents backed by kgraph, a persistent knowledge-graph memory exposed as an MCP server — grounding agents in cited facts and reducing hallucination across sessions.
Publications & Open Source
- Co-authored a paper on post-quantum cryptography (submitted, under review).
- Co-authored a paper on large language model evaluation (submitted, under review).
- Ongoing research on alignment and interpretability of transformer models (targeting a workshop).
- Open-source RLHF, agentic, and interpretability tooling at github.com/starkaritra.
Skills
Languages
- Python
- C
- C++
- Java
- SQL
- JavaScript
ML & AI
- PyTorch
- TensorFlow
- JAX
- Hugging Face
- Scikit-learn
LLM & Applied ML
- Fine-tuning
- RLHF / PPO
- Agentic Systems
- MCP
- RAG
- Interpretability
- Model Compression
Systems & MLOps
- Distributed Training
- DeepSpeed
- Docker
- Linux
- Git
- Weights & Biases
Data & Cloud
- PostgreSQL
- MongoDB
- FAISS
- AWS
- Google Cloud
Open to Collaboration
I'm open to collaborating on papers, projects,
and patents. If your work overlaps any of these areas, let's talk.
LLM Evaluation & Benchmarking
Scalable eval frameworks, metric design, failure-mode and regression analysis.
Interpretability
Probing and understanding transformer internals for transparent models.
Agentic Systems & RLHF
Tool-using agents, planning loops, reward modeling and preference optimization.
Model Compression & Efficient ML
Pruning, distillation, and quantization for fast, deployable models.
ML for Particle Physics
Deep learning on CERN calorimeter data — event classification and scientific imaging.
Quantum Computing
Quantum algorithms and quantum machine learning on the physics frontier.