u/InterestStreet8388

Final-year Stats & Data Science student graduating May 2026, looking for AI/ML Engineer roles in Bangalore.

What I've been working on:

At my internship: Built a bunch of automation systems using n8n and Python. Newsletter system that pulls from RSS feeds, uses LLMs to summarise articles, matches them to clients based on their tech stack via semantic search, and auto-delivers everything. Also built an executive reporting pipeline that aggregates data from Jira, security scans, and vulnerability databases, runs LLM summaries, and spits out professional PDFs, it reduced time spend by engineering team in making reports by 70%.

The more interesting stuff was the agentic systems — a multi-agent RAG chatbot that breaks down questions, fetches relevant GitHub docs, and synthesises answers. And a customer support agent with MCP architecture where LangGraph orchestrates diagnostic tools, suggests fixes, and escalates to engineers when needed. That one reduced support tickets by 40%.

Personal projects (all deployed with interactive Streamlit dashboards):

Banking Analytics Platform — worked with 24M credit card transactions. Built three things: fraud detection model (LightGBM, 88% precision), customer segmentation using GMM clustering to find behavioral patterns, and a recommendation system for credit card products using neural collaborative filtering. Everything's in a dashboard where you can explore predictions and segments interactively.

Supply Chain Optimization — did the full cycle here. Started with data analysis to figure out why deliveries were delayed and costs were high. Built XGBoost models to predict delivery costs and delay probability. Then made a rule-based allocation system that assigns orders to minimize both. Got 20% cost reduction. Dashboard ties it all together with live predictions and recommendations.

E-Commerce Search — this one detects what customers actually mean when they search, then runs hybrid search combining semantic similarity and keyword matching. Also built an automated pipeline that generates product metadata from images using ResNet50. Everything's searchable through the dashboard.

Pharmacy Inventory Chatbot — natural language interface to query inventory across multiple stores. Has hierarchical access — store managers see their store, zonal managers see their zone, master admin sees everything. It's read-only, just runs SELECT queries. Built the whole thing in Streamlit with a chat interface.

Urban Mobility System — this is probably my most complete project. Used 33.6M NYC taxi trips.

Part 1: ETA prediction — trained LightGBM on 21.7M trips with features like traffic lag signals, geographic clustering, and route efficiency. Got 2.3 min error and 86% R². Built a reliability scoring system so you know when to trust the prediction.

Part 2: Demand forecasting — divided NYC into 947 1km² grids and 5 demand zones using k-means clustering. PySpark pipeline predicts hourly pickup demand per zone, 97% R². The dashboard shows a heatmap of how demand shifts across the city hour by hour.

Part 3: Shortest route — used Dijkstra's algorithm on the actual Manhattan road network via OSMnx. Compared GPS-recorded routes vs optimal paths. Found that short trips have way more GPS errors than long ones, which matters if you're building routing systems.

All three modules are in one Streamlit dashboard — you can simulate trips, explore demand patterns, and visualize routes on an interactive map.

Stack I actually use: Python, SQL, PySpark, n8n, LangChain, LangGraph, ChromaDB, scikit-learn, TensorFlow, PyTorch, FastAPI, Streamlit, Tableau, AWS, Databricks

What I'm looking for: AI/ML Engineer roles at Bangalore startups building AI products or internal tools. I know the market is rough right now for freshers, so any referrals or leads would honestly mean a lot.

Hey folks,

Final-year Stats & Data Science student graduating May 2026, looking for AI/ML Engineer roles in Bangalore.

What I've been working on:

At my internship: Built a bunch of automation systems using n8n and Python. Newsletter system that pulls from RSS feeds, uses LLMs to summarise articles, matches them to clients based on their tech stack via semantic search, and auto-delivers everything. Also built an executive reporting pipeline that aggregates data from Jira, security scans, and vulnerability databases, runs LLM summaries, and spits out professional PDFs, it reduced time spend by engineering team in making reports by 70%.

The more interesting stuff was the agentic systems — a multi-agent RAG chatbot that breaks down questions, fetches relevant GitHub docs, and synthesises answers. And a customer support agent with MCP architecture where LangGraph orchestrates diagnostic tools, suggests fixes, and escalates to engineers when needed. That one reduced support tickets by 40%.

Personal projects (all deployed with interactive Streamlit dashboards):

Banking Analytics Platform — worked with 24M credit card transactions. Built three things: fraud detection model (LightGBM, 88% precision), customer segmentation using GMM clustering to find behavioral patterns, and a recommendation system for credit card products using neural collaborative filtering. Everything's in a dashboard where you can explore predictions and segments interactively.

Supply Chain Optimization — did the full cycle here. Started with data analysis to figure out why deliveries were delayed and costs were high. Built XGBoost models to predict delivery costs and delay probability. Then made a rule-based allocation system that assigns orders to minimize both. Got 20% cost reduction. Dashboard ties it all together with live predictions and recommendations.

E-Commerce Search — this one detects what customers actually mean when they search, then runs hybrid search combining semantic similarity and keyword matching. Also built an automated pipeline that generates product metadata from images using ResNet50. Everything's searchable through the dashboard.

Pharmacy Inventory Chatbot — natural language interface to query inventory across multiple stores. Has hierarchical access — store managers see their store, zonal managers see their zone, master admin sees everything. It's read-only, just runs SELECT queries. Built the whole thing in Streamlit with a chat interface.

Urban Mobility System — this is probably my most complete project. Used 33.6M NYC taxi trips.

Part 1: ETA prediction — trained LightGBM on 21.7M trips with features like traffic lag signals, geographic clustering, and route efficiency. Got 2.3 min error and 86% R². Built a reliability scoring system so you know when to trust the prediction.

Part 2: Demand forecasting — divided NYC into 947 1km² grids and 5 demand zones using k-means clustering. PySpark pipeline predicts hourly pickup demand per zone, 97% R². The dashboard shows a heatmap of how demand shifts across the city hour by hour.

Part 3: Shortest route — used Dijkstra's algorithm on the actual Manhattan road network via OSMnx. Compared GPS-recorded routes vs optimal paths. Found that short trips have way more GPS errors than long ones, which matters if you're building routing systems.

All three modules are in one Streamlit dashboard — you can simulate trips, explore demand patterns, and visualize routes on an interactive map.

Stack I actually use: Python, SQL, PySpark, n8n, LangChain, LangGraph, ChromaDB, scikit-learn, TensorFlow, PyTorch, FastAPI, Streamlit, Tableau, AWS, Databricks

What I'm looking for: AI/ML Engineer roles at Bangalore startups building AI products or internal tools. I know the market is rough right now for freshers, so any referrals or leads would honestly mean a lot.