Role: Senior Vision AI Engineer – Lead (Computer Vision & Deep Learning)
Location: Hyderabad (Work From Office)
Experience Required: 3-8 years total with 2–6+ years in Computer Vision / Deep Learning / Perception Systems
About the Role We are looking for a Senior 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
- 2–6+ years practical experience in Computer Vision & Deep Learning.
- Strong hands-on coding with C++ (11/14/17) and Python.
- Expertise in PyTorch, TensorFlow, Keras, and model development for CV tasks.
- Deep understanding of image processing, OpenCV, video analytics, and classical CV (SIFT, SURF).
- Experience with CNN/ResNet/YOLO/VGG, segmentation networks, and sequence models (LSTM/GRU/RCNN).
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.