🚀 Senior Vision AI Engineer – Lead (Computer Vision & Deep Learning)

Location: Hyderabad (Work From Office)

Experience Required: 10–12 years total , with 4–6+ years in Computer Vision / Deep Learning / Perception Systems

About the Role

We are looking for a Lead Vision AI Engineer who can architect, lead, and deliver advanced Computer Vision, Deep Learning, and Perception solutions for real‑time intelligent systems.

This role requires strong technical leadership, hands‑on engineering capability, and proven experience in building production-grade AI products , preferably in ADAS, Autonomous Systems, Smart Cameras, Industrial Vision, or Edge AI .

You will mentor engineers, influence architecture decisions, and work closely with cross‑functional teams to build high‑performance, scalable Vision AI pipelines.

🎯 Key Responsibilities

1. Vision AI / Deep Learning Engineering

• Design, develop, and optimize end‑to‑end Computer Vision pipelines (pre‑processing, inference, post‑processing).
• Build and deploy real‑time models for:
• Object Detection, Tracking, Segmentation, Calibration, and Image Classification
• Train, fine‑tune, and evaluate DL models using PyTorch / TensorFlow / ONNX .
• Develop robust algorithms for image/video processing , including feature extraction and classical CV techniques.

2. Edge AI & Embedded Deployment

• Optimize and deploy models on edge platforms such as NVIDIA Jetson, Qualcomm QRide, DSP/GPU/NPU accelerators.
• Convert and optimize models using TensorRT, ONNX Runtime, QNN, quantization, pruning , and other optimization toolchains.
• Implement high‑performance C++ (14/17/20) modules for embedded CV applications.

3. System Design & Architecture

• Architect scalable, modular CV systems using OOAD, SOLID principles, design patterns, and UML .
• Define dataflows, pipeline architecture, back‑end selection (CPU/GPU/NPU), and integration strategies.
• Collaborate with hardware, systems, and product teams to ensure real‑time performance and reliability.

4. Leadership & Mentoring

• Guide junior and mid‑level engineers through code reviews, architecture discussions, and best engineering practices.
• Take technical ownership of feature modules, delivery quality, and timeline alignment.
• Drive innovation within the team by evaluating new research papers, frameworks, and vision techniques.

5. Deployment & MLOps

• Build production‑ready inference modules, CI/CD pipelines, and testing frameworks for Vision AI models.
• Evaluate KPIs, benchmark performance, and iterate to meet product SLAs.
• Support deployment for global clients and collaborate with offshore/onshore teams.

🧠 Required Skills & Experience

Core Technical Expertise

• 4–6+ years practical experience in Computer Vision & Deep Learning .

Edge & Performance Optimization

• Experience converting models using TensorRT, ONNX , model quantization (INT8/FP16), and acceleration techniques.
• Hands-on knowledge of GPU, DSP, or NPU execution backends and hardware-aware optimizations.

System Engineering

• Strong foundation in data structures, algorithms, memory optimization , multi-threading, and low‑latency systems.
• Experience with UML, OOAD, design patterns (Factory, Strategy, Observer, etc.).

Leadership

• Prior experience mentoring team members or leading feature modules.
• Ability to conduct code reviews, define modeling best practices, and drive engineering excellence.

Additional Good-to-Haves

• Experience building AI products or working in a product-based environment .
• Familiarity with MLOps, Docker, GitLab pipelines, and cloud deployment.
• Exposure to ADAS perception frameworks, autonomous driving stacks, or industrial automation.