SARVESH GANESAN

Lead AI Architect

Architecting enterprise AI platforms that turn data into decisions

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AI/ML

Engineering

Data

Engineering

Cloud

Infrastructure

About Me

Lead AI Architect with 2.5+ years building and shipping a cloud-native enterprise data platform end-to-end — from the agentic chat layer down to the Iceberg lakehouse and Kubernetes runtime images.

Solely architected 12+ production microservices on AWS EKS spanning agentic AI, distributed ETL, data lakehousing, MLOps, and containerized ML runtimes. Built a multi-agent orchestration system with 50+ dynamically-loaded data connectors that collapses hours of manual analytics into seconds of natural-language conversation. Delivered sub-second hybrid RAG at production scale and sub-10s queries over 100M+ row datasets via Spark / Iceberg / DuckDB.

Independently shipped a full-stack CRM platform for an enterprise tea distribution business — storefront, admin dashboard, invoicing, logistics KPIs, and a Claude-powered MCP server exposing 46 administrative tools across 9 business domains.

Multi-Agent AI

8 specialized agents orchestrated by Claude, 60+ tools, hybrid RAG with neural reranking, and cost-optimized model routing

Cloud & Infrastructure

12+ microservices on AWS EKS with Karpenter auto-scaling, 99.9% uptime, and multi-tenant isolation

Data & MLOps

TB-scale Iceberg lakehouse, 69+ data connectors, Spark/DuckDB runtimes, MLflow, and GPU training pipelines

Experience

09/2023 - 03/2026

Lead AI Architect

Groundzero Software Private Limited · Chennai

  • Solely architected and shipped 12+ production microservices end-to-end on AWS EKS — agentic chat platform, data provider API, ETL orchestrator, Iceberg REST catalog, MLOps control plane, and Spark/DuckDB/PyTorch/TensorFlow/scikit-learn runtime images
  • Built a Claude-orchestrated multi-agent chat system with intent-aware agent routing, per-tenant tool selection, and SSE-based streaming — enabling enterprise users to query databases, build dashboards, submit ETL jobs, and manage notebooks entirely through natural language
  • Engineered a dynamically-loaded connector architecture scaling data access to 50+ sources (Snowflake, BigQuery, Redshift, Postgres, MongoDB, Kafka, S3, Salesforce, MySQL, Oracle) with role-based authorization and schema introspection caching
  • Reduced RAG query latency by 90% (10s → sub-1s) using hybrid vector + full-text search with Cohere neural reranking, running over pgvector on production traffic
  • Delivered sub-10s analytic queries on 100M+ row datasets by architecting a TB-scale Apache Iceberg lakehouse backed by a gRPC-based REST catalog with schema evolution, time-travel, and multi-warehouse federation
  • Cut LLM inference costs via three-tier model routing (Opus/Sonnet/Haiku) with Anthropic prompt caching, per-request token tracking, and automatic downgrade for deterministic tool calls
  • Designed the ETL orchestration layer handling job submission, multi-job chaining, container log streaming from EKS pods, and dataset lineage — across three pluggable compute engines on Karpenter-provisioned spot nodes
  • Stood up the MLOps control plane from scratch — MLflow with custom JWT auth, Jupyter lifecycle management on EKS, fine-tune job dispatch across 7+ LLM providers, and GPU training containers for PyTorch, TensorFlow, and scikit-learn
  • Achieved 99.9% uptime across multi-tenant deployments via Karpenter just-in-time node provisioning, graceful pod lifecycle handling, and tenant-scoped compute isolation
  • Authored an end-to-end pytest suite covering dashboard rendering, visualization generation, and agentic chat flows with browser automation and screenshot-based regression validation
06/2023 - 08/2023

Software Trainee

SCI-BI Software Solutions Private Limited · Chennai

  • Developed and maintained data visualizations and dashboards using Power BI and Tableau
  • Participated in client meetings to gather requirements and translate them into technical specifications
  • Created reports and presentations, enhancing clients' data-driven decision-making

Technical Arsenal

AI & LLM

  • AWS Bedrock
  • Strands Agents
  • LangChain
  • RAG Systems
  • Cohere Embed & Rerank
  • Prompt Caching
  • Multi-Agent Orchestration

Cloud & Infrastructure

  • AWS EKS
  • S3
  • EC2
  • ECR
  • Lambda
  • CloudWatch
  • Docker
  • Kubernetes
  • Karpenter

Data Engineering

  • Apache Spark
  • Apache Iceberg
  • DuckDB
  • PostgreSQL
  • pgvector
  • Redis
  • Pinecone
  • MongoDB

Development & MLOps

  • Python
  • FastAPI
  • Flask
  • REST APIs
  • gRPC
  • SSE Streaming
  • MLflow
  • PyTorch
  • TensorFlow

Featured Projects

Sree Rajalakshmi CRM

End-to-end CRM and operations platform I built solo for a tea distribution business — customer storefront, admin dashboard, GST-compliant invoice generator, Razorpay checkout, WhatsApp order bot, and a Model Context Protocol (MCP) server exposing 46 administrative tools across 9 domains so Claude can run the business conversationally.

  • FastAPI
  • React
  • MCP Server
  • Razorpay
  • AWS Lambda
Visit Live Site

SRE Tea MCP Server

Production-grade Model Context Protocol server I shipped to let Claude Desktop, Claude Code, and custom MCP clients operate a live e-commerce business. Exposes 46 tools across orders, products, customers, inventory, invoicing, logistics, churn analytics, and warehouse management with automatic token refresh, parallel aggregation, and structured error recovery.

  • MCP
  • Node.js
  • Claude
  • STDIO Transport
  • JWT
View on GitHub

PlannerAgent

Conversational planning agent I built on LangGraph that handles 10+ turn dialogues without context loss, asks targeted clarifying questions, and versions plans across sessions. Ships with 12+ templates, interactive Gantt charts, multi-format import (Trello, CSV, Markdown), and a dual-provider LLM backend across Bedrock and Anthropic.

  • LangGraph
  • Claude
  • AWS Bedrock
  • Python
  • Session State
View on GitHub

Data Quality Checker

GPU-accelerated Python library I authored and published on PyPI (data_pilot_checker) for automated data integrity validation at scale. Runs missing-value, duplicate, outlier, type, and range checks on cuDF when a CUDA GPU is present, with transparent Dask fallback for CPU-only environments.

  • cuDF
  • Dask
  • RAPIDS
  • PyPI
  • GPU
View on GitHub

Tech Stack

  • Python
  • AWS Bedrock
  • Docker
  • PostgreSQL
  • Strands Agents
  • Apache Spark
  • Kubernetes
  • FastAPI
  • MLflow
  • Iceberg

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Let's Connect

Looking for an AI architect who ships production systems, not prototypes?

sarveshganesan2002@gmail.com
+91 8668063705
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