About Us

Frinks AI is a AI-powered machine vision platform that enable manufacturers to seamlessly integrate advanced AI for quality control and visual inspection, ensuring unparalleled quality and consistency in manufacturing. With a focus on addressing unique challenges in the sector, we’re pioneering research on domain-specific, lean, and productive AI models that enhance operational efficiency, optimize processes, and drive innovation. Our scalable platform automates production processes with high accuracy, making it a trusted solution for global manufacturers.

Role Overview

As our Applied AI Lead, you will own the end-to-end development, software architecture, and continuous evolution of the Frinks Vision Platform. In this role, you will lead and mentor a team of computer vision and software engineers, driving the full AI product lifecycle from feature design to client release. You will oversee containerized edge deployments, maintain high code quality and software architecture standards, and manage operational execution on manufacturing factory floors.

You will ensure product stability, handle complex field deployments, and occasionally conduct targeted R&D; to integrate practical AI advancements into our platform.

Key Responsibilities

1. AI Product & Platform Ownership: Direct the end-to-end software engineering, architecture, and scaling of the Frinks Vision Platform, turning computer vision capabilities into reliable, production-ready product features.

2. Team Management & Engineering Leadership: Lead, mentor, and manage a team of computer vision and software engineers—establishing clean code standards,



robust software design patterns, code reviews, and project delivery schedules.

3. Deployment, Containerization & Field Operations: Oversee containerized edge deployments, NVIDIA workstation configurations, TensorRT/ONNX runtime optimizations, and real-time performance on active customer production lines.

4. Field Reliability & Customer Feedback Loops: Drive product stability by resolving live deployment edge cases and ensuring zero-downtime performance on factory floors.

5. Occasional Applied R&D;: Conduct targeted research, benchmarking, and prototyping to evaluate modern vision models, loss functions, or algorithms when specific customer inspection challenges require custom solutions.

6. Cross-Functional Alignment: Partner directly with the CTO and hardware/field teams to translate manufacturing client specifications into scalable AI platform features.

Required Qualifications

1. AI Product Development: 5+ years of experience leading end-to-end development of AI products, taking platform architecture from design and prototyping to field release and continuous maintenance.

2. Engineering Leadership: Proven track record of managing, mentoring, and guiding a team of computer vision and software deployment engineers.

3. Deployment,



Containerization & Field Operations: Oversee containerized edge deployments, NVIDIA workstation configurations, TensorRT/ONNX runtime optimizations, and real-time performance on active customer production lines.

4. Production Deployment & Operations: Direct experience managing active production-line deployments, troubleshooting live customer feedback loops, and ensuring platform reliability on factory floors.

5. Frameworks & Core Tech: Python, OpenCV, and related libraries for practical application development.

6. Edge Optimization: Success optimizing models and pipelines for real-time edge execution using TensorRT, ONNX Runtime, or C++ integrations.

7. Architectures: Practical grasp of modern computer vision techniques—detection, segmentation, classification, and OCR—as well as classical image-processing methods applied to real-world industrial data.

Preferred / Nice-to-Have (Optional)

1. 3D Point Cloud & Spatial Vision: Hands-on experience with 3D vision, point cloud processing (e.g., Open3D, PCL), and spatial geometry applied to real-world industrial or CAD/part-inspection datasets.

2. Research & Publications: Track record of publishing in top-tier computer vision or machine learning conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, WACV) focused on applied vision, detection, or anomaly analysis.

3. Full-Stack & Platform Engineering: Practical knowledge of backend services (FastAPI, gRPC, REST APIs) or lightweight frontend dashboards (React, Vue, Streamlit) to help oversee end-to-end platform tooling and client interfaces.

📌 Head of Artificial Intelligence (Bengaluru)
🏢 Frinks AI
📍 Bengaluru