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Enterprise SDKs · Engineering software

I’m Tryaksh, a Product Manager building enterprise SDKs for complex engineering workflows across design, CAE, EDA, and robotics.

Tryaksh Gupta, Product Manager, Modeling & Simulation

Background

From engineering systems to product work.

I started in mechanical engineering, drawn to problems where math, software, and physical systems come together. At IIT Roorkee and the University of Michigan, I worked across computational physics, numerical methods, finite elements, simulation, and high-performance computing.

At Michigan, I contributed to a Toyota Research collaboration on solid-state battery degradation. That work shaped how I think about engineering software at scale: models are only useful when they help people reason about complex systems more clearly and realistically.

Tesla brought me closer to production. I worked on battery-cell manufacturing equipment, where engineering design decisions had to hold up under real operating constraints — reliability, repeatability, deployment, and speed.

Today, at Dassault Systèmes Spatial, I work on enterprise SDKs used by engineering teams in design, simulation, and robotics around the world. My work sits between customers, developers, and R&D: understanding technical workflows, shaping APIs, guiding evaluations, and helping complex technology become easier to deploy and adopt.

I also build independently. NextGen News is an AI product I started for young readers in India, where I work across product, engineering, AI workflows, validation, and distribution.

IIT Roorkee

Mechanical engineering

Michigan × Toyota

Computational research and multiphysics

Tesla

Battery manufacturing and deployment

Spatial

Enterprise SDKs, simulation, and robotics

NextGen News

Independent AI product building

How I work

Close to customers, product, and engineering.

01

Understand the real workflow

I stay close to customers, sales, and field teams during discovery and evaluations. The goal is to understand what they are building, where the workflow breaks, and what matters beyond the initial feature request.

02

Prototype before scaling

I use AI-assisted development to turn ambiguous needs into something tangible: a C++ POC, an API sketch, or a lightweight application. Prototypes expose tradeoffs, clarify requirements, and reduce the risk of spending months on the wrong capability.

03

Scale with engineering

Once the need is validated, I work with R&D on architecture, APIs, roadmap, enablement, and GTM. The goal is to scale what we have learned into something customers can adopt.