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Summary

SWE with proven industry skills in developing and deploying real-world ML solutions, cloud-native backend applications and microservices design. Passionate about architecting scalable, reliable, and maintainable data intensive applications that leverage AI and timeless engineering principles.

Skills

Programming Languages
Expert in Python, SQL. Familiar with C/C++, Flutter.
AI / ML
LangChain, LangGraph, RAG, TensorFlow, PyTorch, NumPy, Pandas, Hugging Face.
Data
ETL, Apache Beam, BigQuery, SQL/NoSQL, GCS, Kafka, PySpark, Redis. Familiar with Airflow.
Backend
REST (FastAPI), Microservices, Websockets, gRPC,, Auth/Authz, Load Balancing, Rate Limiting, Scalability.
Containerization & CI/CD
Docker, Docker Compose, GitHub Actions, Python workspace management.

Professional Experience

Founder & Principal Engineer — Lever AI

Full-time · July 2025 – Present

  • Founded a consulting and SaaS development LLC specialized in AI-powered, data-driven software solutions.
  • Showcase Project: Designed and shipped a trivia-like estimation game to (Google Play, App Store) featuring:
    • AI-native ETL pipeline: Used LLM APIs for question generation, enrichment, embedding for deduplication, and grounded answering. Used semaphores for mitigating API rate limits. Stored questions in Postgres with pgvector.
    • High consistency for a multi-player game mode using atomic Firestore transactions.
    • Low-latency fair question assignment using optimized schema design, indexing, and SQL queries.
    • Full observability via end-to-end distributed tracing with OpenTelemetry, correlating Flutter frontend events to FastAPI backend spans via Cloud Trace and Cloud Logging.
    • GitFlow CI/CD pipelines with automated unit, integration, and smoke testing, and dev/prod deployments.
    • Leveraged AI coding agents (Claude, Gemini) to accelerate development while maintaining full architectural oversight.

Machine Learning Engineer III — Pison Technology

Full-time · Oct 2022 – July 2025 · 2 yrs 10 mos

  • Promoted from MLE to MLE III in April 2025.
  • Ran 1,200+ TensorFlow experiments for sleep stage classification in TensorFlow, improving the baseline macro F1-score by 15%. Mitigated class imbalance with Focal and Contrastive Loss. Designed a custom attention layer for feature mining, improving performance while reducing the size of the feature set by an order of magnitude.
  • Developed real-time biosignal gesture detection models. Wrote Cythonized extension modules, and vectorized bottleneck functions, significantly improving the speed of online prediction and offline modeling.
  • Led a “Hackaweek” project with two colleagues to implement a state-of-the-art domain-invariant classification network with adversarial training in PyTorch, following the approach outlined in its original manuscript.
  • Led the adoption of BigQuery for analytics pipelines. Decided partitioning and clustering for efficient queries.
  • Deployed and monitored various services using Google Model Registry and Cloud Functions.
  • Improved the speed of a batch ETL pipeline using Apache Beam by more than 190 folds.
  • Combined standalone Python repos into a single uv workspace, allowing teams on different operating systems to seamlessly cooperate, reducing the size of deployed containers, and streamlining code installation, testing, and deployment.
  • Maintained cross-team communication to ensure consistency and optimal integration of ML solutions.

Research Assistant — University of Massachusetts Lowell

Jan 2021 – June 2022

  • Implemented Reinforcement Learning agents and environments for cooperative multi-agent Search and Rescue tasks.
  • Implemented object detection training and inference pipelines. Used Generative AI for data augmentation.

Education

M.S. Computer Engineering · GPA 4.0 · UMass Lowell, 2022

B.S. Electrical Engineering · GPA 3.8 · UMass Lowell, 2021