---
title: "Aniruddha Shrikhande - AI Specialist in Bengaluru | Tal"
description: "Meet Aniruddha Shrikhande, AI Engineer | Production AI Systems | System Design | Multi-Agent AI | RAG | Voice AI | LLMOps based in Bengaluru. AI stack includes antigravity, claude code, and claude."
image: "https://tal.club/meet/aniruddha-shrikhande/share-image"
---

# Aniruddha Shrikhande on Tal

## Headline

AI Engineer \| Production AI Systems \| System Design \| Multi\-Agent AI \| RAG \| Voice AI \| LLMOps

## About

I’m an AI Engineer working on production AI systems at AIM\. Over the past year, I’ve built and deployed multi\-agent workflows, RAG pipelines, LLM evaluation systems, and real\-time AI applications for enterprise clients like AMD, Tata Elxsi, EXL, and Carrier\. Most of my work involves figuring out how to make LLM\-based systems actually reliable in production not just impressive in demos\. I spend most of my time working across agent orchestration, retrieval systems, evaluation pipelines, structured outputs, FastAPI backends, and cloud infrastructure\. I’ve also worked with AWS, GCP, Redis, vector databases, and LangGraph to build scalable AI workflows that can handle real\-world usage\. What interests me most is the engineering side of AI which includes System Design, Reliability, Observability, Latency, and the Tradeoffs involved in deploying AI systems at scale\. Core Areas: · Agentic AI · RAG · Multi\-Agent Systems · Voice AI · LLMOps · FastAPI · Python · AWS · GCP · Redis · Vector Databases

## Location

Bengaluru

## AI stack

antigravity

claude code

claude

cursor

elevenlabs

gemini

## Work experience

AI Specialist at AIM, Apr 2026 \- Jul 2026 · 4 mos, • Leading AI engineering initiatives across enterprise client engagements, architecting production\-grade multi\-agent systems, LLM infrastructure, and scalable GenAI workflows\. • Designing and deploying stateful multi\-agent orchestration pipelines using LangGraph with conditional routing, shared memory propagation, cyclic feedback loops, and tool\-augmented execution\. • Owning end\-to\-end GenAI delivery lifecycle which includes architecture, evaluation pipelines, guardrails, deployment, observability, and production monitoring\. • Building scalable LLM systems integrating RAG pipelines, structured output validation, async processing, and retrieval optimization\. • Collaborating with product, engineering, and client stakeholders to translate business requirements into production\-ready AI systems\.

Associate AI Specialist at AIM, Feb 2025 \- Apr 2026 · 1 yr 3 mos, Delivered AI engineering across multiple enterprise client projects spanning GenAI infrastructure, LLMOps, evaluation systems, and multi\-agent orchestration\. AMD \[Developer Engagement Platform\] • Architected and deployed a FastAPI\-based gamification engine supporting 10\+ activity types, Redis\-backed deduplication, tiered referral systems, and real\-time leaderboards serving 100k\+ users with sub\-50ms latency\. • Engineered a 7\-stage conversion funnel analytics platform using PostgreSQL materialized views and RBAC\-enabled versioned APIs reducing dashboard query latency to under 200ms\. • Built a serverless OAuth\-integrated social verification pipeline across LinkedIn, X, and YouTube using Celery \+ Redis async workflows, eliminating 90% of manual review operations\. Tata Elxsi \[AI\-Based Coding Evaluation Platform\] • Designed a seniority\-banded LLM evaluation framework combining deterministic rule\-based scoring, JSON schema validation, retry handling, and hallucination detection for high\-volume technical assessments\. • Developed a structured prompt\-chaining boilerplate generation engine supporting multiple programming languages, reducing candidate environment setup time by \~15 minutes per session\. EXL \[Gamified Learning Platform\] • Orchestrated a stateful LangGraph multi\-agent pipeline with A2A collaboration, dependency mapping, conditional routing, and self\-correcting feedback loops for synthetic dataset generation workflows\. • Implemented human\-in\-the\-loop approval checkpoints alongside Faker\-backed constrained synthesis and validation agents enforcing schema contracts and statistical consistency checks\. • Automated scalable synthetic data generation across finance, healthcare, and retail domains reducing manual dataset preparation effort by \~70%\.

Generative AI Engineer at AIM, Oct 2024 \- Jan 2025 · 4 mos, Developed production\-ready GenAI features as part of AIM's engineering team while transitioning to full\-time\. • Architected a real\-time AI video interview system with streaming avatar responses, structured LLM scoring pipelines, async FastAPI backend, and resilient S3 multipart uploads with crash recovery — ensuring consistent session handling under unstable network conditions\. • Gained hands\-on experience in LLMOps, prompt engineering, and deploying LLM\-backed APIs in a fast\-paced product environment\.

## Education

Bachelor of Technology \- BTech, Electrical, Electronics and Communications Engineering, Vishwakarma Institute of Technology

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

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