Career Snapshot

Period Role Company
2022–Present Simulation Lead, EMEA A123 Systems GmbH, Germany
2022 Lead – Simulation & Analysis (Sr. Manager) Volvo Trucks R&D, India
2021–2022 Performance Engineer Caterpillar R&D, India
2015–2021 Technology Lead / Senior Engineer Mercedes-Benz R&D, India
2013–2015 Deputy Manager, R&D Hero MotoCorp, India
2011–2013 Member, R&D TVS Motor Company, India
2009 Research Intern Victoria University, Melbourne

Full experience and CV →


Patents


The Short Version

Mechanical engineer by training (IIT Kanpur, M.Tech + B.Tech). 15 years across automotive R&D — ICE systems, EV modeling & simulation, battery electrochemistry, and now ML/AI tooling for engineering workflows.

The consistent thread: build the infrastructure that makes the problem tractable. Not just solve the current instance, but reduce the cost of solving the next one.

Currently: Sr. Product Engineer(Simulation Lead, EMEA) at A123 Systems (Germany). Translate OEM requirements into simulation scope across DE/IN/CN teams, deliver final validation authority for EMEA deliverables.

2 patents · 3 simulation teams built · 10+ engineers led · 60% model dev time reduction at MBRDI


What I Actually Do


Education

M.Tech & B.Tech — Mechanical Engineering, IIT Kanpur (2006–2011)

M.Tech Thesis: Critical Velocity and Standing Waves in High-Speed Rotating Tires

The thesis established the instinct that’s recurred ever since: the interesting physics lives at the boundary — at the critical velocity where wave propagation onset changes the contact mechanics, at the SoC where lithium plating onset shifts the safety limit, at the dependency that breaks the codebase when modified.


The Arc

IIT Kanpur → TVS → Hero → Mercedes-Benz → Caterpillar → Volvo → A123. Not a straight line, but a consistent pattern: go deep into a domain, build the infrastructure, then move to a harder problem.

The AI tooling work isn’t a career pivot — it’s the same instinct applied to the analysis workflow itself. The ANSYS APDL macros that cut pre-processing time by 50% in 2012 and the FAISS-based codebase indexer in 2023 are the same kind of move: the workflow is an engineering problem, and it should be treated like one.

Full background and story →


Outside Work

Engineering trains you to decompose problems. Painting trains you to see what’s actually there, not what you think is there. These aren’t the same skill, and the second one is harder.

Sketchbooks, observational drawing, occasional writing. The habits that slow down perception in a useful way — the same attention that catches a model that “should work” but doesn’t.

Fountain pen user. Analog watch collector. Reader who can’t accept black boxes.

“You can’t draw what you don’t see. You can’t model what you don’t understand.”