Artificial Intelligence Engineer

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

Location: Hyderabad (Work From Office)

Experience Required: 1012 years total, with 46+ 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 realtime intelligent systems.

This role requires strong technical leadership, handson 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 crossfunctional teams to build highperformance, scalable Vision AI pipelines.

Key Responsibilities

1. Vision AI / Deep Learning Engineering

• Design, develop, and optimize endtoend Computer Vision pipelines (preprocessing, inference, postprocessing).
• Build and deploy realtime models for:
• Object Detection, Tracking, Segmentation, Calibration, and Image Classification
• Train, finetune, 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 highperformance 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, backend selection (CPU/GPU/NPU), and integration strategies.
• Collaborate with hardware, systems, and product teams to ensure realtime performance and reliability.

4. Leadership & Mentoring

• Guide junior and midlevel 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 productionready 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

• 46+ years practical experience in Computer Vision & Deep Learning.
• Strong handson 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 lowlatency 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.