AI · Data · Software Systems

Software Engineer

I design and build intelligent systems that turn complex problems into useful products.

I build data-intensive and AI-powered systems across machine learning, retrieval, analytics, and backend infrastructure. My work spans data pipelines, production APIs, LLM-enabled applications, search & ranking workflows, and research tooling.

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01About

The engineer behind the systems

I build at the intersection of data, intelligence, and software, combining strong foundations with the curiosity to explore what has not been built yet.

I am a software and data engineer who turns ambiguous, high friction problems into dependable systems across data infrastructure, machine learning, backend engineering, and applied AI. I care about making difficult systems clear, measurable, reliable, and useful.

Building something that works is only the beginning. The craft is finding the right structure, creating what did not exist before, and iterating until the result feels inevitable.

More About Me
System ProfileSynced
Core FocusIntelligent Data and AI Systems
Engineering StyleFirst Principles · Evidence · Iteration
Current DirectionProduction AI and Data Platforms
Preferred ProblemsAmbiguous · Data-Intensive · High-Leverage
Technical StrengthsPipelines · Retrieval · Ranking · APIs · MLOps
Current LocationNew York City Metro
Opportunity StatusOpen to Full-Time Data, AI/ML, and Software Roles
02Experience

Mission Path

A connected journey through the roles and systems I have worked on. Select any station to expand its details.

  1. Station 01

    Station 1

  2. Station 02

    Station 2

  3. Station 03

    Station 3

  4. Station 04

    Station 4
Explore all my experience

Teaching, course-assistant, research, and additional industry roles.

Graduate Course Assistant — Big Data · NYU Tandon School of Engineering
03Projects

Selected Missions

A set of systems I have designed and built. Each opens into a detailed case study.

Featured SystemPrototype
Data & Platform Systems

SignalLake

A local-first log analytics platform built with FastAPI, partitioned Parquet, and DuckDB

I designed and built an end-to-end log analytics platform that validates incoming events, preserves raw records in JSONL, transforms them into Hive-partitioned Parquet, and serves operational metrics through DuckDB and FastAPI. On a benchmark of 1 million events across 192 files, partition pruning reduced data scanned from 86.7 MB to 6.3 MB.

FastAPIPythonPydanticDuckDBApache Parquet

1M events · 192 files · 92.7% less data scanned

View case study
Benchmarked 1,000,000 events across 192 Parquet files with identical analytical results across partitioned and unpartitioned layouts. The pruned query touched 8 of those 192 files.
Prototype
Search, Retrieval & Applied AI

ExpertMatchAI

During my Software Engineering internship focused on AI/ML and data infrastructure at Global Futures Group, I built the retrieval and ranking infrastructure for an expert-matching platform covering 12,168 profiles. The system combined semantic search, BM25 lexical retrieval, structured filters, and tunable ranking signals, with average search latency under 200 ms, p95 latency under 350 ms, and full index rebuilds in under 60 seconds.

Next.jsTypeScriptFastAPI

12,168 profiles · <200 ms average · p95 below 350 ms

Details
Concept
MLOps & Production Engineering

DevOps Swarm AI

I built a DevOps review assistant on top of Google Cloud's agent-starter-pack scaffold and implemented the same four capabilities through three orchestration patterns: a CrewAI crew, a LangGraph pipeline, and a ReAct-style tool-calling agent. I connected the tool-calling version to FastAPI, added structured failure handling, traced requests through Traceloop and Google Cloud Trace, and added a GitLab CI pipeline for the test suite.

CrewAILangGraphLangChain

CrewAI, LangGraph, and ReAct tool-calling architectures

Details
Prototype
Data & Platform Systems

NYC Subway Foot-Traffic Forecasting

I designed and built the real-time streaming infrastructure for a subway foot-traffic forecasting system, including a realistic Kafka producer, a Spark Structured Streaming pipeline into MongoDB, and live Random Forest inference for station entries and exits. Working with a teammate on the historical side, we analyzed approximately 13 million MTA turnstile records, where Random Forest regression and classification models reached approximately 2,700 RMSE, below 4.8% MAE, and 93.36% classification accuracy.

Apache KafkaSpark Structured StreamingPySpark

13M historical records · RMSE ≈2,700 · 93.36% accuracy

Details
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04Skills

Technical Constellation

Clusters of capabilities and how they connect. Select a cluster to explore it.

LanguagesActive

I use Python most often for data engineering, machine learning, backend services, automation, and research workflows. TypeScript and JavaScript support full-stack product development, while C and C++ strengthen my systems and performance-oriented foundations. SQL is central to the way I work with analytics, transformation, and querying across structured data systems.

PythonTypeScriptJavaScriptSQLCC++
05Education & Research

Foundations

Academic background and research that shaped how I approach problems.

New York University logo

New York University

M.S. in Computer Engineering

Sept 2024 – May 2026New York, NY

GPA 4.0 / 4.0

My graduate work at New York University strengthened the foundation behind the systems I build today, spanning machine learning, data-intensive engineering, and computer systems. It gave me the technical depth to move comfortably between theory, infrastructure, and applied intelligent products.

Selected Coursework
Machine LearningDeep LearningDatabase SystemsBig DataHigh-Performance ComputingComputer System ArchitectureData and AI+ more coursework
National Institute of Technology Rourkela logo

National Institute of Technology Rourkela

B.Tech in Electronics and Communication Engineering

Nov 2020 – May 2024Rourkela, India

CGPA 8.03 / 10

My undergraduate foundation at NIT Rourkela began in Electronics and Communication Engineering, where I developed the analytical and systems-thinking mindset that later shaped my path into software, data, and AI systems.

Selected Coursework
ProgrammingData Structures and AlgorithmsOperating SystemsMachine IntelligenceProbability and StatisticsData Communication NetworksInformation Theory and CodingSoft Computing+ more coursework
Research Foundations

NYU Chunara Lab

Reproducible text-data pipelines, newspaper analysis, LLM-assisted labeling, and research tooling.

DICE Lab, NYU

LLM fine-tuning, LoRA/PEFT experimentation, research infrastructure, and model experimentation.

Principles

How I Think

A few principles that guide the way I design and build systems.

Principle 01 — Lead

Build for clarity

I structure systems so that another engineer can understand how they work, why key decisions were made, and where to make changes safely. Clear interfaces, explicit assumptions, and useful documentation are part of the implementation, not work saved for later.

Validate with evidence

I avoid treating intuition as proof. I define what success means, inspect intermediate outputs, test assumptions, and use measurable results to guide each iteration. Improvements should be demonstrated, not merely claimed.

Design for reliability

I plan for failure before it becomes an incident. That means predictable behavior, rerunnable workflows, validation, observability, safe fallbacks, and enough operational context to understand what happened when something goes wrong.

Keep learning

I stay curious about new tools and ideas, but I do not adopt them only because they are new. I study the underlying principles, test where they create real value, and continuously deepen the technical range needed to solve harder problems.

Communicate decisions

I make trade-offs, constraints, and assumptions visible so teams can reason about a system together. Good communication reduces repeated debate, improves handoffs, and helps future decisions build on context rather than guesswork.

Turn complexity into usable systems

I break ambiguous problems into understandable parts, connect data, intelligence, and software into one coherent workflow, and refine the result until it is useful to the people who depend on it.

Create with curiosity

I bring curiosity and creative thinking to technical work, especially when the problem is ambiguous or the obvious solution is not enough. I explore alternatives, test ideas quickly, and shape practical systems that balance originality with reliability.

Contact

Let’s build something meaningful.

I'm open to full-time opportunities, thoughtful collaborations, and conversations about building reliable data, AI, and software systems. Whether you're hiring, exploring an idea, or working on a difficult technical problem, I'd be glad to connect.

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