Manufacturing & Mechanical Engineer · Ph.D. · Founder, FitEx Industrial Technologies LLC
Santa Clara, California · Industrial engineering · Quality systems · Manufacturing analytics · AI-assisted engineering
I turn manufacturing problems into measurable engineering decisions and practical digital tools.
I combine manufacturing experience from high-volume automotive production, precision-instrument manufacturing, industrial product development and research with working engineering applications. My projects emphasize traceable formulas, transparent assumptions, synthetic demonstration data and engineering review.
| Project | Practical engineering purpose | Demo / Source |
|---|---|---|
| FitEx Industrial Engineering Calculator | Takt, cycle time, JPH, OEE, LHU, staffing, greenfield capacity design, Yamazumi, MODAPTS, scenario analysis and PDF reporting | Launch app · Code |
| Manufacturing Process Excellence Toolkit | Process capability, PFMEA, SPC, OEE, gauge studies and manufacturing intelligence | Code |
| Design Review Miner | Retrieve generalized engineering lessons and similar failure cases | Code |
| PFMEA Document Generator | Produce structured PFMEA and corrective-action documentation | Code |
| MODAPTS Time Study | Industrial work-measurement demonstrations | Code |
| Manufacturing Operations System | Exploratory factory operations management workflows | Code |
Data integrity: Public demos use illustrative or generalized datasets and are not presented as verified customer outcomes. Site calculations require process-specific verification before operational use.
In development — not yet a production service. An engineering-focused assistant combining tested deterministic calculation tools with language-model interpretation, guided data intake and human engineering review.
- Step 1: Analyze production demand, shift capacity, bottleneck cycle and OEE with auditable formulas.
- Step 2: Summarize likely constraints and suggest what measurements to collect next.
- Step 3: Prepare a draft manufacturing diagnostic for engineer approval.
- Architecture: Python calculation engine + optional OpenAI Agents SDK / Hugging Face model-backed explanations. No credentials stored in public code.
Commercial goal: Pilot a scoped Manufacturing Capacity & Labor Optimization Assessment for manufacturers that need a defensible process improvement plan. Discuss a pilot.
| Manufacturing and industrial engineering | Quality and reliability | Data and software |
|---|---|---|
| Process design, general assembly, end-of-line, line balancing, MODAPTS, time study, ergonomics, OEE, takt, capacity and labor planning | PFMEA / DFMEA, SPC, Cp/Cpk, Gage R&R, 8D, MRB, CAPA, inspection planning | Python, SQL, Power BI, MATLAB, AI/ML, retrieval, analytics dashboards, engineering reports |
- Thermo Fisher Scientific: Technical documentation and operational excellence support in instrument manufacturing (2024–2026).
- Tesla: Manufacturing process engineering and high-volume automotive general-assembly / end-of-line operations (2018–2024).
- Mezewir Industrial Solutions: Founder/engineering leadership in industrial and agricultural machinery development.
- IIT Delhi: Ph.D. in Mechanical Engineering; forming processes, materials characterization and finite-element analysis.
- Graduate AI/ML education: In progress.
Practical engagement: Process capacity review → bottleneck analysis → line balancing and labor planning → validated engineering recommendations.
- Employers: Explore the tools above as evidence of analytical and implementation capability.
- Manufacturers: Request a scoped diagnostic or engineering support engagement.
- Collaborators: Discuss industrial AI workflows, manufacturing data modeling and tool validation.
FitEx website · LinkedIn · Email · GitHub
Engineering methods are only as valuable as the decisions they improve. My focus: reliable calculations, clear communication and measurable implementation.