Lead Computer Vision Engineer

AegisVision; Hyderabad; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

AegisVision in Hyderabad is seeking a Senior Vision AI Engineer to architect, lead, and deliver real-time computer vision and perception solutions for intelligent systems. You will mentor engineers, influence architecture, and work closely with cross-functional teams to build scalable Vision AI pipelines.

The role requires hands-on expertise in DL, CV, and edge deployment, with experience on NVIDIA Jetson and accelerators, and strong C++/Python coding.

Qualifications

• 8+ years experience in computer vision and deep learning.
• Strong C++ (11/14/17) and Python coding skills.
• Proven expertise with PyTorch, TensorFlow and Keras for CV tasks.
• Experience with edge deployment on NVIDIA Jetson, DSP/NPU backends and related tools.

Responsibilities

• Architect and deliver real-time Vision AI/Deep Learning systems.
• Mentor engineers; drive code reviews and architecture discussions.
• Optimize models for edge/embedded deployment and hardware backends.
• Drive MLOps pipelines and production-grade inference modules.

Skills

Computer Vision

Deep Learning

C++ programming

Python

Tools

PyTorch

TensorFlow

Keras

OpenCV

Job description

Computer Vision AI Engineer - Lead (IC + Leadership Role)

Location: Hyderabad (Work From Office)

Experience: 8-12 years

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

• 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.
• 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.
• 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.
• 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.
• 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.