Toronto, Canada · Python ecosystem engineer · Open to new work

Python across the stack — APIs, RAG, and the data behind them.

Sarthak Shah. Nine years of Python engineering. Django and FastAPI in production, PostgreSQL and SQLite underneath, and LangChain / LangGraph for RAG and AI app building — shipped in enterprise and client work, not a side project.

Practicing Apache PySpark and Airflow for big data, Pandas, NumPy and Scikit-learn for analysis, and picking up Go's goroutines for concurrency. If it's Python, it's probably somewhere in my stack.

Sarthak Shah

What I have built

ai / enterprise-rag

Agentic RAG for enterprise knowledge retrieval

Morgan Stanley engagement · 2025

Contributed to an OpenAI-based RAG system with an agentic, tool/function-calling workflow that routes queries to the correct retrieval source and returns grounded, citation-backed answers. Applied prompt engineering for constrained generation, and exposed retrieval and generation steps as Python service endpoints for downstream apps.

PythonRAGAgentic AIFastAPI

ai / rag-assistant

LangChain RAG assistant, built and shipped

Independent client work

Built a production-use RAG assistant for insurance agency clients: policy and coverage documents embedded into a vector store, with LangChain orchestrating multi-step retrieval and response chains. Served behind a FastAPI endpoint for natural-language policy queries, with Pytest coverage on retrieval relevance before rollout.

LangChainFastAPIRAGPytest

python / aircraft-configurator

Aircraft surface configurator

Libellule Monde · 2023 to now

A Django platform that analyses aircraft exterior regions and estimates the time, resources and cost of surface treatments. I led it from user interviews through to production, and own the PostgreSQL schema and query performance underneath it as the dataset has grown.

DjangoPostgreSQLDjango ORMDocker

python / parts-labels

Parts label generation service

Libellule Monde · ongoing

A FastAPI service that generates enterprise-specific labels, stickers and plates for aircraft parts, with search across a large part-number database. Redis sits in front as a caching layer, which cut database round trips and made search suggestions fast enough to be useful while typing.

FastAPIPostgreSQLRedisDocker

python / data-pipelines

ATA documentation ETL

Libellule Monde

Pipelines that pull structure out of ATA-standard aerospace documentation and load it into the analytics platform, so maintenance and repair teams can search and reason over it instead of opening PDFs. I own the query paths, indexes and storage strategy underneath, tuned against real usage as data volume grew.

PythonETLPostgreSQLDjango ORM

python / trade-surveillance

Trading activity extraction

First Derivative · 2025

Python jobs that pull trader activity from NAS file systems, databases and Splunk and turn unstructured logs into structured JSON: timestamped queries, user actions, originating screen. The Risk team uses that feed for anomaly detection.

PythonPandasSplunkRegex

python / device-integration

Medical device data integration

Bacancy Technology · 2022 to 2023

FastAPI and Flask endpoints connecting Philips Biotel devices to a client portal, with secure storage and real-time processing of streaming device data. Django applications alongside it, hosted on EC2 with Nginx and Gunicorn.

FastAPIFlaskDjangoDocker

What I am practicing right now

AI app building & RAG

LangChain · LangGraph

Already shipped in client and enterprise work. Now deepening the parts that make it production-grade: retrieval that returns the right chunk, and graphs that let an agent take more than one step without losing the plot.

  • LangChain retrieval and response chains, moving to LangGraph for stateful, multi-step agent flows
  • Constrained generation and citation-backed answers, so output stays inside what was retrieved
  • Serving it like any other service: FastAPI endpoints, Pytest on retrieval relevance, not a notebook
LangChainLangGraphRAGFastAPI

Big data with Apache tools

PySpark · Airflow

My ETL work so far has been scripts and schedulers I wrote myself, feeding real production pipelines. I am moving that onto the tools built for the job: orchestration and transformation that scale past one machine.

  • Airflow DAGs: dependencies, retries, backfills, and what to do when a task fails at 2am
  • PySpark for transformations that no longer fit comfortably on one machine
  • Pandas, NumPy and Scikit-learn for the analysis layer on top
Apache AirflowPySparkPandasScikit-learn

The RAG and AI work above has shipped to real clients and an enterprise engagement. The big-data tooling is the layer I'm actively building on top of production ETL I already own — and Go's goroutines are on the side, for when Python isn't the right tool. I'd rather say that plainly than have it come out in an interview.

Where I have worked

Aug 2025 — now

Senior Software Developer, Libellule Monde

Aircraft data and analytics platform. PostgreSQL ownership, Redis caching, automated daily digests to email and Teams via Power Automate.

Apr 2025 — Aug 2025

Senior Software Engineer, First Derivative

Trade surveillance data extraction, distributed job deployment across regions, monitoring with Grafana Loki and Splunk.

Oct 2023 — Apr 2025

Senior Software Engineer, Libellule Monde

Led the configurator platform and the parts label service. Ran Gunicorn and Caddy on EC2 across production and development.

Feb 2022 — Oct 2023

Senior Software Engineer, Bacancy Technology

Django and FastAPI applications for medtech and enterprise clients, on EC2 behind Nginx and Gunicorn, with Docker and Kubernetes.

Oct 2020 — Feb 2022

Embedded Software Engineer, Xylem Inc.

Python applications for water controllers, using CPython, Modbus, MQTT and JSON-RPC between sensors and controllers.

2018 — 2020

A&W Engineering Works · L&T Technology Services

Verification suites for ultrasound medical hardware, and Python test automation for mobile and OTT apps. Two spot awards at L&T.

Education

Lambton College · Indus University

Post Graduation in Full Stack Software Development, Toronto. B.Tech in Electrical and Electronics, India.

What I work with

Language

Python, first and mostly. SQL alongside it

Web frameworks

Django, Django REST Framework, FastAPI, Flask

Databases

PostgreSQL, SQLite, Redis, Django ORM

AI & RAG

LangChain, LangGraph, retrieval-augmented generation, AI app building, prompt engineering

Big data (practicing)

Apache PySpark, Apache Airflow

Data analysis

Pandas, NumPy, Scikit-learn

Cloud and infra

AWS (EC2, S3, Lambda), Docker, Kubernetes, Gunicorn, Nginx

Testing

Pytest, Unittest, Selenium, Postman

Learning

Go — goroutines and concurrency patterns