🚀
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.
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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.