---
title: "Devesh Jaluka - AI/ML Engineer : Service Delivery Partner Wipro in Hyderabad | Tal"
description: "Meet Devesh Jaluka, AI/ML Engineer focused on RAG and FastAPI development, Claude Certified Architect. based in Hyderabad. AI stack includes anthropic apis, claude code, and codex. Explore public work links including github and deveshjaluka.space."
image: "https://tal.club/meet/devesh-jaluka/share-image"
---

# Devesh Jaluka on Tal

## Headline

AI/ML Engineer focused on RAG and FastAPI development, Claude Certified Architect\.

## About

Currently an AI/ML Engineer at Apple and pursuing a Bachelor of Technology in Computer Science at Vellore Institute of Technology, contributing to innovative AI solutions\. Previous roles include developing modular AI pipelines and chatbot systems at Algorizz Technologies, leveraging LangChain, RAG, and advanced prompt engineering techniques\. Research experience includes building a CNN\-LSTM model for land cover prediction using satellite imagery data at VIT\. Proficient in Retrieval\-Augmented Generation \(RAG\), React\.js, and FastAPI, with a strong foundation in computer science principles and a focus on advancing AI applications\.

## Location

Hyderabad

## AI stack

anthropic apis

claude code

codex

hugging face

langfuse

langgraph

## Work experience

AI/ML Engineer : Service Delivery Partner Wipro at Apple, Mar 2026 \- Present · 9 mos, Hyderabad, Telangana, India, Architected and shipped a production agentic NL2SQL analytics platform over a high\-dimensional, multi\-million\-record longitudinal data warehouse; designed a multi\-stage LangChain workflow for question decomposition, structured query planning, scope validation, self\-correcting SQL generation, execution, and adaptive analytical outputs, achieving 98% SQL accuracy on a curated golden evaluation set\. – Engineered the analytics platform’s production reliability and data layer through PostgreSQL migration and parity validation, connection pooling, read\-only execution, multi\-layer SQL guardrails, query bounds, and structured tracing; built a 2K\+ case regression suite and a standalone MCP server for secure, tool\-based schema discovery and analytical querying\. – Designed and shipped a production multimodal RAG system for complex presentation\-heavy knowledge sources, implementing 3\-stage cascading retrieval with progressive metadata relaxation, vector fallback, and cross\-encoder reranking, achieving 0\.75 MRR@10 on retrieval evaluations\. – Reduced the RAG system’s end\-to\-end P50 latency by 67% \(90s → 30s\) by redesigning the inference path with non\-blocking async execution, persistent connection reuse, concurrent model racing with early cancellation, token\-level SSE streaming, and adaptive context compression for large retrieved contexts\. – Built a reusable GenAI evaluation and API benchmarking framework to compare production AI systems across curated datasets, combining fuzzy matching, embedding\-based semantic similarity, and LLM\-as\-a\-Judge with repeated\-run consistency analysis to quantify accuracy, semantic agreement, response stability, and detect reliability regressions\. \-Service Delivery Partner:\- Wipro

AI/ML Engineer at Wipro, Nov 2025 \- Present · 1 yr 1 mo, Hyderabad, Telangana, India

AI Engineer at Algorizz Technologies, May 2025 \- Nov 2025 · 7 mos, Remote, •Developed a modular agent\-based context aware AI pipeline using LangGraph, LangChain, RAG Quadrant, OpenAI Embeddings, Perplexity API for research , and Azure OpenAI to dynamically generate high\-quality PowerPoint presentations\. •Designed and implemented a custom decision graph using LangGraph to coordinate multiple specialized agents across a generation workflow\. •Applied advanced prompt engineering and retrieval techniques to enhance content accuracy, relevance, and structure\. •Built a chatbot generation system leveraging LangChain, RAG architecture, and web scraping to create custom knowledge\-based conversational agents\. •Integrated web content via scraping tools like BeautifulSoup and Playwright, processed it into vector databases using FAISS, and embedded responses with Azure OpenAI\. •Fine\-tuned the Salesforce BLIP vision\-language model on the MIMIC\-CXR dataset to improve chest X\-ray captioning performance for Indian patients\. •Incorporated CLIP embeddings, domain\-specific data augmentation, and custom captioning techniques to achieve near state\-of\-the\-art results\. •Utilized tools such as PyTorch, Hugging Face Transformers, Grad\-CAM, and TensorBoard for model training, evaluation, and interpretability\.

Research Intern at Vellore Institute of Technology, Jul 2024 \- Dec 2024 · 6 mos, Developed a CNN\-LSTM model to predict dynamic land cover changes using 39 years of satellite imagery data for Chatham, Massachusetts\. Preprocessed annual satellite images to 256×256 resolution and applied image normalization for efficient model training\. Contributed to the publication demonstrating the model’s ability to capture spatial and temporal patterns for environmental management\.

Project Intern at ETHNUS, Aug 2023 \- Nov 2023 · 4 mos, Remote

## Education

Bachelor of Technology, Computer Science, Vellore Institute of Technology

Junior Collage, Science and Mathematics, Delhi Public School, Ranchi

## Public links

- github: https://github\.com/dj216185
- deveshjaluka\.space: https://deveshjaluka\.space/

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

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