About Me

I am a backend Python developer working in a financial organization where data accuracy and operational reliability are critical. My role focuses on building systems that transform raw partner data into validated, reconciled, and report-ready datasets used by business and operations teams.

My daily work includes developing ETL pipelines, validating incoming data, reconciling financial records, optimizing database queries, and building backend APIs that power dashboards and internal applications used by non-technical users.

I also build backend services capable of integrating AI APIs for automated document processing and intelligent assistance features.

What I Can Help With

Replacing manual Excel/CSV workflows with automated Python pipelines

Diagnosing and fixing slow SQL queries — indexing, query plans, stored procedures

Building REST APIs (FastAPI/Flask) for internal tools and dashboards

Processing and validating large financial datasets (millions of rows)

Integrating LLM/AI APIs into backend services with structured output parsing

Setting up scheduled batch jobs, email reports, and failure alerting

Production System Overview

Business Uploads Data› Python Validation Pipeline› Reconciliation Engine› MySQL Database› Backend API Services (Flask/FastAPI)› Dashboards and Automated Email Reports

These systems are used by internal operations and reporting teams on recurring reporting cycles and require data reliability, validation, and auditability.

Job Experience

NJ Asset Management Pvt Ltd

Jun 2024 – Present

Python Developer – Data Analytics

System Context: Internal financial reporting and reconciliation system used by business and operations teams.

Responsibilities

  • Developed Python ETL pipelines to ingest, validate, and reconcile financial datasets
  • Built Flask/FastAPI backend services powering dashboards and automation workflows
  • Migrated Excel processes into structured backend systems
  • Automated scheduled reporting with Cron jobs and SMTP email summaries
  • Implemented validation checks and reconciliation logic with idempotent batch processing

Impact

~60%Reduced manual preparation effort
~50%Improved dashboard/report performance
Higher ReliabilityImproved consistency of operational reporting

Business teams rely on these pipelines and dashboards for recurring reporting and data verification.

NJ Asset Management Pvt Ltd

Jan 2024 – Jun 2024

Data Science Intern

Built early dashboard prototypes and backend workflow foundations that were later scaled into production systems.

Production Engineering Practices

Structured logging for debugging and audits

Validation checks on incoming datasets

Idempotent reconciliation workflows

Scheduled automated jobs

Failure detection and alert notifications

Data consistency verification

Skills

All skills below are grounded in day-to-day production work at NJ Asset Management.

Backend Development

Python, FastAPI, Flask, REST APIs, Pydantic, Jinja2 - used daily to build internal services, automation scripts, and reporting backends.

Database & SQL

MySQL, PostgreSQL, SQLAlchemy, query optimization, indexing, stored procedures - production tuning that improved dashboard performance by 50%.

ETL & Data Processing

Pandas, NumPy, custom ETL pipelines, batch processing - ingesting, validating, reconciling millions of financial records on recurring cycles.

Automation & Reliability

Cron jobs, Celery task queues, Redis caching, SMTP email automation, structured logging, failure alerts - reduced manual effort by 60%.

AI & LLM Integration

OpenAI API, LangChain, RAG pipelines, embeddings, ChromaDB/FAISS vector search — building backend services with AI-powered features.

Tools & DevOps

Docker, Git, Linux, pytest, CI/CD basics, AsyncIO, workflow scheduling, OpenAPI/Swagger documentation.

AI-Enabled Backend Systems

I build backend services that integrate AI APIs into applications.

Calling LLM APIs from backend services Structured response parsing Retry and error handling Document ingestion pipelines Embeddings generation Vector search retrieval Retrieval-augmented generation (RAG) Automated document Q&A systems
Upload Document› Text Chunking› Embeddings› Vector Store› Retrieve Context› Send to LLM› Structured Response

Side Projects

Supplementary technical projects - my core skills and experience come from full-time production work above.

Payment Fraud Detection System

Problem: Detect fraudulent transactions within large financial datasets.

Implementation

  • Preprocessing and feature engineering on 6.3M+ transactions
  • Model training and evaluation with XGBoost
  • FastAPI inference endpoint for real-time predictions
  • Containerized deployment with Docker

Dataset: 6.3M+ transactions

Result: ROC-AUC approximately 0.96

Demonstrates: Handling large datasets and exposing predictive models via backend API.

PythonPandasScikit-learnXGBoostFastAPIDocker

ESG Portfolio Analysis

Work: Financial dataset analysis and statistical modeling of ESG impact on stock performance.

Result: R² approximately 0.62

Demonstrates: Data processing pipelines and analytics backend support.

PythonPandasNumPyMatplotlibScikit-learn

Education

Bachelor of Engineering in Computer Engineering

Sarvajanik College of Engineering and Technology (SCET), Surat

Oct 2020 - May 2024

CGPA: 8.00

Certifications

Oracle Cloud Infrastructure Certified Data Science Professional

Oracle

Issued 2025

View Badge ↗

Currently Open To

Python Backend Developer, Backend Engineer, or Backend + AI Integration roles. Targeting product companies, fintech, SaaS, or data-driven startups across India.

Immediate joiner · Open to relocation anywhere in India · Comfortable with on-site, hybrid, or remote setups

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