AI Platform & Performance Engineer · Pune, India

Shubham Pagare I turn complex AI systems into business outcomes.

Senior engineer with 5+ years building scalable AI systems, LLM observability, and enterprise performance across Azure, AWS, and GCP — for Fortune 500 clients.

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Core stack AWS Azure GCP Python LLM LangChain LangGraph Langfuse
About

Engineer, translator, builder

I sit where business goals meet technical reality — and make sure both win.

I turn ideas into shipped, scalable systems — and make sure the engineering serves a real outcome. Most of my work lives in the gap between stakeholders and development: clarifying what's needed, surfacing tradeoffs early, and making complicated things feel simple.

Business context, not just the code
Ideas → shipped reality
Bridge stakeholders and engineering
Make complex systems feel simple
120%
response-time gain on WebSocket bottlenecks
10×
execution speedup, Python → C++
~6 min
analysis cut from a 1-hour task
100%
tool-licensing savings vs LoadRunner
How I work

The way I think

The instincts behind the tools — how I approach systems, diagnostics, and hard problems.

Architect Mindset

See the whole system before the parts — design for scale, trace the data, anticipate where it breaks.

Deep Diagnostics

Comfortable in the weeds: thread dumps, memory leaks, latency hotspots, token-level profiling.

Problem Solving

Break ambiguous, multi-layer issues into root causes — then ship the fix that actually holds.

Stakeholder Clarity

Translate complex engineering into decisions leaders can act on.

Experience

Where I've shipped

A run through the roles, the clients, and the measurable outcomes — from AI agent platforms to deep performance forensics.

Dec 2024 — Present

Senior Performance & AI Engineer

Waynautic Technologies
Client: PwC US— AI-based product on Azure Cloud
  • Designed and deployed multi-agent AI workflows with LangChain, LangGraph, and Azure OpenAI to automate QA and business processes.
  • Built a custom E2E trace tracking tool with Langfuse for deep LLM pipeline visibility — surfacing latency hotspots, token inefficiencies, and failure points.
  • Led AI performance engineering across LLM analysis, load-testing strategy, and Azure optimization, driving measurable cost reduction.
  • Identified critical architectural bottlenecks and shaped design decisions in direct review with PwC US stakeholders.
Sept 2023 — Nov 2024

Performance Engineer

Zensoft Services
Client: PwC US
  • Achieved 120% response-time improvement and ~0.1% error rate resolving multi-layer WebSocket performance issues.
  • Built a custom JMeter + Selenium framework replacing LoadRunner TrueClient — 100% savings on tool licensing.
  • Automated test execution and analysis on Azure ADO with Python, cutting insight-delivery time by 50%.
Aug 2021 — Sept 2023

Performance Engineer

NTT Data Services
Client:UNIQLO Uniqlo (UQ)
  • Led an in-house open-source monitoring & auto-analysis tool, reducing a 1-hour task to ~6–7 minutes.
  • Diagnosed memory leaks, packet drops, and microservice issues across AWS and GCP, significantly improving production stability.
  • Built precision JMeter load models and implemented database replication for improved reliability.
  • Star Award (Foresight & Hard Work) and KK Award (Outstanding Performance of the Year).
Jan 2021 — June 2021

Engineering Intern

Softnautics LLP
  • Migrated a codebase from Python to C++, achieving a 10× improvement in execution speed.
Toolkit

What I work with

From agentic orchestration to thread-dump forensics — the stack behind the outcomes.

Generative AI & Agentic Systems
LangChain LangGraph Langfuse AI Agent Dev LLM Observability E2E Trace Analysis Prompt Engineering
Performance & Monitoring
JMeter Gatling Azure Load Testing LoadRunner Grafana Datadog Dynatrace CloudWatch App Insights
Cloud & DevOps
Azure AKS AWS ECS GCP Jenkins Azure ADO Docker Git / GitHub Microservices
Programming & Analysis
Python JavaScript React Django C++ Thread / Heap Dump AWR LLM Token Profiling
Writing

Notes from the edge

Thinking out loud on AI systems, context, and making agents useful in the real world.

Aug 8, 20267 min readAI Systems · Architecture

Why AI systems pass the demo and die in production

Most AI products don't fail because the model is weak. They fail because the architecture assumed a clean world — complete docs, linked alerts, tidy ownership — that never existed outside the slide deck.

Read essay
Aug 6, 20266 min readAI Agents · MCP

Context Builder: why coding agents fail on large repos — and what to build instead

Agents don't need another chat window. They need a budgeted, task-shaped context pack — Map, Facts, Evidence — assembled ahead of the session so they stop rediscovering the monorepo every time.

Read essay
Foundations

Education & recognition

DEGREE

B.Tech, Computer Science (AI/ML)

MIT ADT University, Pune
CGPA 8.32/10 · Distinction · Aug 2017 — July 2021
Star Award — Foresight & Hard Work · NTT Data KK Award — Outstanding Performance of the Year · NTT Data
CERTIFICATIONS
2024Site Reliability Engineering — Google / Coursera
2019Data Science Orientation — IBM / Coursera
Contact

Let's build something observable.

Open to roles and collaborations in AI platform engineering, LLM observability, and performance. The fastest way to reach me is below.

shubhamp2017@gmail.com Download +91 70281 19806 LinkedIn GitHub