Job Description

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

Location: Hyderabad (Work From Office)

Experience Required: 3-8 years in Computer Vision / Deep Learning / Image Processing Engineer / 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.