I'm a grad student at Northeastern University finishing my MS in Data Analytics Engineering.

I like building systems that move, transform, and learn from data — from data pipelines and ML infrastructure to full-stack tools that people actually use. Most of the time, I’m building something around data engineering, AI/ML, or platform/backend systems.

Outside of work and school, I’m usually exploring new ideas, gaming with friends, trying things on Hack The Box, or learning something new just because it looks interesting.

I’m currently looking for full-time roles in Data Engineering, AI/ML Engineering, and Data Platform / SWE.

• I enjoy building projects that solve real problems, not just demos
• I’m especially interested in data systems, LLM/RAG applications, ML infra, and scalable backend design
• I like understanding how things work under the hood and improving them
• I’m big on self-growth, hands-on learning, and building things I’m genuinely excited about

LangChain ChromaDB SentenceTransformers Airflow Langfuse RAGAS FastAPI Docker

Built an LLM-powered Q&A system over 2M+ automotive reviews with query decomposition, vector retrieval, and agent-style answer generation. Reached ~90% faithfulness on RAGAS with response times under 2 seconds, and used Langfuse to track retrieval drift and latency so debugging felt like engineering, not guessing.

PySpark Spark SQL dbt XGBoost MLflow Delta Lake Kafka

Designed a distributed ML pipeline across 5+ telemetry sources and 10M+ records, with lag-aware joins and dbt contracts across 20+ feature tables. Improved reliability for downstream ML teams, reduced compute cost by 45%, and shipped a model that reached 0.73 ROC-AUC with a ~20% precision@k lift.

FastAPI React PostgreSQL Redis JWT/RBAC Docker GitHub Actions pytest

Built a full-stack task platform with 20+ REST endpoints, authentication, RBAC, and caching. Optimized schema and Redis-backed reads to cut API latency by ~35%, and set up CI/CD with 88% test coverage so releases stayed predictable.

Kafka MySQL PostgreSQL Great Expectations Tableau

Developed ETL workflows for 120k+ IoT sensor records across 12 device streams and engineered time-series HRV features for mental health analytics. Also built 12+ Tableau dashboards to help analysts connect sleep, stress, and biometric trends without digging through raw data.

Data & Platform — PySpark · Databricks · Delta Lake · dbt · Airflow · Kafka · Great Expectations · Snowflake
ML / AI — PyTorch · TensorFlow · Scikit-Learn · XGBoost · LangChain · RAG · LoRA/PEFT · ONNX · MLflow · Langfuse
Cloud / Infra — AWS · Azure · Docker · Kubernetes · GitHub Actions · Terraform
Languages — Python · SQL · Java · JavaScript · C++ · Bash
Databases — PostgreSQL · MongoDB · Snowflake · BigQuery · Redshift · ChromaDB · FAISS

• building projects around RAG, data pipelines, and ML systems
• exploring better ways to combine LLMs + structured data
• looking for opportunities where I can build impactful data/AI systems at scale

I like spending time building what I’m passionate about, especially projects around AI/ML, data, and systems. I also enjoy gaming with friends, solving technical challenges, and constantly finding ways to improve myself.