Prabhakar Lal profile photo 1 on Tal

Prabhakar Lal

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

Pune City

< 1 year of work experience

FPL Technologies
Jan 20267 mosPresent

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.

Prabhakar’s AI Stack

hugging face
hugging face
kiro
kiro
langfuse
langfuse
langgraph
langgraph
PydanticAI
PydanticAI

Education

Shoolini University

Bachelor of Technology - BTech, Computer Science

Shoolini University

20224 yrs2026

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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

Prabhakar Lal - Graduate Engineering Trainee in Pune City | Tal