---
title: "Prabhakar Lal - Graduate Engineering Trainee in Pune City | Tal"
description: "Meet Prabhakar Lal, AI Backend Engineer @OneCard | LLMs, Production RAG & Agentic AI (LangGraph, MCP) | Docker | Postgres | Redis based in Pune City. AI stack includes hugging face, kiro, and langfuse."
image: "https://tal.club/meet/prabhakar-lal/share-image"
---

# Prabhakar Lal on Tal

## Headline

AI Backend Engineer @OneCard \| LLMs, Production RAG & Agentic AI \(LangGraph, MCP\) \| Docker \| Postgres \| Redis

## About

I am a Machine Learning Engineer passionate about building scalable AI systems that bridge cutting\-edge research with real\-world applications\. With a strong foundation in Python, Deep Learning, Computer Vision, and NLP, I specialize in designing and deploying models that deliver measurable business impact\. I have worked on projects ranging from real\-time emotion detection using CNNs to intelligent recommendation models, combining robust engineering with practical deployment\. I enjoy solving problems end\-to\-end — from data preprocessing and model optimization to API integration and frontend\-backend connectivity \(React\) — ensuring that AI solutions are not only accurate but also production\-ready\. Currently, I am seeking opportunities to contribute as a Machine Learning Engineer / AI Engineer in product\-driven companies, where I can apply my expertise to build impactful, high\-performance AI solutions at scale\.

## Location

Pune City

## AI stack

hugging face

kiro

langfuse

langgraph

PydanticAI

## Work experience

Graduate Engineering Trainee at FPL Technologies, Jan 2026 \- Present · 7 mos, Pune City, Building scalable Python backends and deploying ML infrastructure for complex financial operations\. • Financial RAG Development: Built an end\-to\-end retrieval system for complex financial PDFs and tabular data, integrating Docling for OCR, Alibaba embeddings, and a Vector DB\. • Retrieval Optimization: Engineered a hybrid search pipeline, implementing a BGE\-Large cross\-encoder \(reranker\) to filter context and drastically reduce LLM hallucinations\. • Production Deployment: Scaled the AI architecture for production by deploying Hugging Face TEI for GPU\-accelerated model hosting and integrating Redis caching to minimize query latency\. • Asynchronous Backend Engineering: Maintained and optimized a high\-performance Sanic Python backend for internal customer service apps, resolving critical bugs to ensure high API availability\.

## Education

Bachelor of Technology \- BTech, Computer Science, Shoolini University

[View the original profile](https://tal.club/meet/prabhakar-lal)

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