I enjoy working at the intersection of Machine Learning, Data Engineering, and real-world engineering problems — with a focus on building systems that are measurable, deployable, and useful beyond a notebook.
🛰️ Contrail · Event-Time Stream Processor for Flight Telemetry
Python·FastAPI·Kafka (Redpanda)·TimescaleDB·Redis·Docker·Prometheus/Grafana
Contrail ingests live and synthetic ADS-B flight telemetry through Kafka and processes it with correct event-time semantics — the three questions that separate a real streaming system from a Kafka-consumer tutorial: how do you handle data that arrives late, how do you scale under burst load, and how do you guarantee correctness after a crash.
| Event misattribution (naive → watermark) | Peak lag under 6x burst | Replay determinism |
|---|---|---|
| 13.66% → 0.00% | 4.2x lower (adaptive + shedding vs. static) | Byte-identical across repeated runs and mid-stream crashes |
Every claim benchmarked against a naive baseline, on real ADS-B data
and controlled synthetic chaos (out-of-order, duplicate, and late
events) — full reproduce commands and exact configs in BENCHMARKS.md.
🔍 How Contrail works
Live OpenSky feed / synthetic generator
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Kafka (Redpanda) — partitioned, idempotent producers
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Watermark-based event-time windowing (handles out-of-order + late data)
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Lag-trend adaptive controller (scales workers, sheds load under sustained overload)
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TimescaleDB (durable) + Redis (cache/pub-sub)
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FastAPI — REST + WebSocket, JWT-authenticated
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Prometheus/Grafana observability
🧪 Engineering highlights
- Watermark-based windowing with a configurable allowed-lateness bound and a late-event side output, benchmarked against a naive processing-time baseline on 88,199 events.
- Lag-trend adaptive control loop that scales and sheds Kafka consumer workers based on the rate of change in lag, not a static threshold — cuts peak lag 4.2x and p99 latency ~6x vs. a static pool during a 6x burst.
- Deterministic replay harness proving byte-identical output across repeated runs and mid-stream consumer failures, backed by idempotent database writes.
- FastAPI service with JWT auth, rate limiting, REST + WebSocket APIs,
live OpenSky ingestion, full Prometheus/Grafana observability, and a
chaos-tested crash-recovery path — all runnable with one
docker compose up. - Documented, honest limitations (not overclaimed): live multi-partition
watermarking, shedding-under-sustained-overload — see
DESIGN_DECISIONS.md.
🧠 CI-Brain · Continuous Integration Intelligence Platform
FastAPI·PostgreSQL·SQLAlchemy·Alembic·React + TypeScript·Docker·GitHub Actions·Google Gemini·Render
CI-Brain analyzes CI pipelines to make software testing faster and more intelligent — covering the three core problems large engineering orgs actually solve: which tests to run, which tests are flaky, and what caused a wall of failures.
| Tests eliminated | Wall-clock time saved | Flaky tests caught |
|---|---|---|
| 57.5–98.9% | 9.5–58.2% | 4 / 4 · 0 false positives |
Benchmarked against toolz, a real open-source Python library,
across a range of simulated single-file and multi-file diffs — ranges
reflect diff size, not a single run.
🔍 How CI-Brain works
CI data
↓
Coverage / file-to-test dependency graph
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Test Impact Analysis
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Statistical Flaky-Test Detection
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Failure Clustering
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Evidence-grounded LLM Explanation
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Developer Dashboard
🧪 Engineering highlights
- Test Impact Analysis falls back to the full suite when confidence is insufficient.
- Flaky-test detection uses behavioral evidence rather than a simple failure-rate threshold.
- Failure clustering groups failures by likely root cause before asking the LLM for a summary.
- 110 tests with 98%+ branch coverage.
- FastAPI backend, PostgreSQL database, React + TypeScript dashboard, Dockerized deployment and GitHub Actions integration.
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India Electricity Demand Analytics Python · PostgreSQL · SARIMAX · Power BI · GitHub Actions End-to-end ELT pipeline analyzing India's electricity demand across 65,178+ state-level daily records (2020–2025).
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Cost-Sensitive Churn Prediction scikit-learn · FastAPI · Docker · GitHub Actions · Render Optimizes the decision threshold around the actual economics of a retention campaign, rather than accuracy alone.
Profit figures use illustrative campaign-cost assumptions ($50 offer cost / $500 customer LTV / 30% save rate) on the IBM Telco dataset — the framework is the point, not these exact dollar values. |
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🕵️ FraudLensCredit Card Fraud Detection XGBoost · Neural Networks · Jupyter End-to-end fraud detection including temporal drift analysis, model robustness and business cost optimization — not just AUC. |
NILM — Non-Intrusive Load Monitoring Signal Processing · scikit-learn · REFIT Identifies household appliances from aggregate power consumption by combining Electrical Engineering, signal processing, and ML. Random Forest Macro F1: 0.56 |
Core-EE work applying signal processing and circuit analysis to problems that actually show up on a plant floor — condition monitoring and power quality, not just simulation for its own sake.
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Bearing Fault Diagnosis via Envelope Spectrum Analysis Signal Processing · Hilbert Transform · scikit-learn · CWRU Dataset Diagnoses rolling-element bearing faults from raw vibration by demodulating the structural resonance — physics first, ML as a cross-check, not the method.
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Plant Feeder Harmonic Filter Design (IEEE 519-referenced) Python · Circuit Simulation · Power Systems Simulates a 415V industrial feeder with a VFD load to show how a naive capacitor bank for power-factor correction can make harmonic distortion worse — and why a detuned filter is the standard fix.
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A modular, plugin-based computer-vision platform for gesture-controlled applications and AI demos.
Current focus:
- Plugin-based application architecture
- Shared camera pipeline
- Computer-vision inference
- CPU-efficient processing
- ONNX Runtime integration
Core
Python · C++ · SQL
Backend / Systems
FastAPI · Kafka (Redpanda) · PostgreSQL · TimescaleDB · Redis ·
Docker · GitHub Actions · Prometheus/Grafana
ML / AI
scikit-learn · TensorFlow · XGBoost · OpenCV · ONNX
Data / Signal Processing
Pandas · NumPy · SciPy · Hilbert Transform · Power BI
Frontend / Visualization
React · TypeScript · Power BI
National Institute of Technology Rourkela B.Tech — Electrical Engineering · Graduating 2028
- Backend & Distributed Systems Engineering
- Machine Learning Engineering
- Data Engineering
- Computer Vision
- AI-powered developer tools
Open to SDE, Machine Learning and Data Engineering
internships · Placement 2028
LinkedIn
· GitHub