Vision AI Engineer

Roles & Responsibilities

We’re looking for a Vision AI Engineer to help build next‑generation video intelligence systems powered by modern vision‑language models. You’ll work across the full video understanding stack—combining multimodal foundation models with established analytics approaches to deliver reliable, production‑ready AI solutions.

Key Responsibilities

· Build end‑to‑end video analytics pipelines using vision‑language models.

· Fine‑tune and adapt foundation models for domain‑specific video understanding.

· Integrate VLM reasoning with traditional video analytics components.

· Develop and maintain inference pipelines for video and multimodal data.

· Deploy and optimize models for scalable, high‑performance production use.

· Diagnose model issues and strengthen system stability and robustness.

· Collaborate with product and engineering teams to deliver AI-driven features.

Required Qualifications

· Strong background in computer vision, video analytics, or AI engineering.

· Practical experience with vision‑language and video‑language architectures.

· Hands-on experience fine‑tuning, evaluating, and deploying deep learning models.

· Familiarity with foundation models such as CLIP‑based architectures, BLIP/BLIP‑2, and open‑source VLMs (e.g., Qwen‑VL, InternVL).

· Proficiency in Python and deep learning frameworks (e.g., PyTorch).

· Solid understanding of CNNs,Transformers, and attention mechanisms.

· Experience with model optimization techniques (quantization, batching, memory strategies).

· Experience deploying models on Docker, cloud platforms, or on‑prem GPU systems.

Preferred Qualifications

· Master’s or PhD in Computer Vision, Machine Learning, AI, or related fields.

· Experience with real‑time or near‑real‑time video analytics.

· Familiarity with traditional VA methods (detection, tracking, motion analysis).

· Exposure to MLOps workflows(versioning, CI/CD, monitoring).

· Interest in modern VLM and video understanding research.

What We Offer

· Opportunities to work on cutting‑edge multimodal AI technologies.

· Ownership of production‑scale video intelligence pipelines.

· A collaborative environment that blends research and engineering.

Tell employers what skills you have

TensorFlow
Machine Learning
VA
Pipelines
Video Analytics
Rollout
Computer Vision
Keras
PyTorch
Good Communication Skills
Python
Docker
Product Development
Linux
Able To Work Independently
C++