PCB Component Identification & Visual QC support for development of AI based PCB Inspection System

Job ID: 40203799

Budget: ₹75,000 – ₹150,000 INR

We are developing an AI-based Automated PCB Inspection System and are currently working with a large set of assembled PCBs that are already fabricated and available at the QC-ready stage. We are looking for a seasoned PCB inspection and assembly expert to perform structured technical verification and visual quality assessment of these boards.

The PCBs under review are high-reliability, multi-layer assemblies (4 to 28 layers) with dense routing and mixed component technologies (SMT + through-hole). Board sizes range from 50 × 50 mm up to 500 × 500 mm, across ~400 PCB variants.

The main help required is two-fold:

1️⃣ Component Verification Against BOM

Identify and verify every mounted component against the provided BOM

Check part number, package, footprint, polarity, and orientation

Flag missing, wrong, substituted, or suspect components

Verify markings where readable

Highlight any mismatch between footprint and populated part

2️⃣ Detailed Visual & Assembly Quality Inspection
Inspection should focus on:

Component placement accuracy and alignment

Orientation and polarity correctness

Tombstoning, skew, lift, or tilt

Solder joint quality (bridging, insufficient/excess solder, cold joints, voids where visible)

Pad / track visible defects and surface damage

Residues, contamination, or cleaning issues

Mechanical mounting and fixing quality

Visible process and assembly defects

A defect checklist aligned with IPC-A-610 / IPC-J-STD-001 acceptance criteria will be shared and should be used as the baseline reference.

Site Location: Hyderabad, Balanagar

Deliverables (PDF / Spreadsheet formats):

Annotated component verification log with designator-wise status and discrepancies

Visual QC defect report with marked images, defect class, and severity level

Photo evidence of critical findings

Suggested corrective actions or rework notes

A concise, repeatable inspection checklist/flow for in-house QC reuse

Timeline
1-2 weeks is preferred so we can decide next actions (AI dataset tagging, rework, or acceptance).