Mechanical Design Engineer - AI Training Project (India)

What You'll Do5

• Build expert-level mechanical design scenarios that stem from real CAD, tolerancing, and production workflows.
• Assess AI-generated answers for technical accuracy, manufacturability, and fit with standard engineering practice.
• Deliver structured written feedback that highlights flaws and supports model improvement.
• Work without a fixed schedule or minimum hour target, choosing when and how much to contribute.
• Use depth in one sub-domain, such as parametric CAD, GD&T and DFM, or FEA simulation, to guide evaluations.

Requirements9

• Senior-level industry background with a portfolio of manufactured designs and complex GD&T or FEA work. A strong portfolio outweighs an advanced degree on its own.
• BS or MS in Mechanical Engineering, paired with practical industry experience.
• Demonstrated depth in one area: parametric CAD (SolidWorks, CATIA, Fusion 360, or Onshape), GD&T and design for manufacturing, or FEA and simulation.
• Indian work authorization.
• Strong written communication and close attention to detail.
• Ability to explain complex mechanical design concepts in plain language.
• Ability to work on your own in a remote, asynchronous setup.
• Proficiency with multiple CAD platforms and simulation environments.
• A portfolio that shows designs carried from concept through manufacturing.

Who Should Apply

Senior mechanical design engineers with a portfolio of manufactured parts and deep work in parametric CAD, GD&T, or FEA fit best. Handshake AI also welcomes applicants who meet two of the three core qualifications, such as a strong industry record plus one sub-domain specialty. The role suits engineers who can explain tolerance decisions, DFM tradeoffs, and simulation results in writing, with Indian work authorization and comfort in an asynchronous remote setup. Candidates with only academic projects, no evidence of production designs, or little writing experience tend to score lower. A common rejection point is a portfolio that lists software but omits parts taken from concept through manufacturing, or feedback samples that stay vague about why an AI answer fails on manufacturability.

Salary Insight