AI/ML Engineer

Core Responsibilities

Computer Vision Product Development

Lead the development of computer vision models and pipelines for analyzing

images and videos.

Design systems for tasks such as:

object detection and recognition

image classification

video understanding and event detection

segmentation and visual feature extraction

OCR and document vision

Build end-to-end vision processing pipelines including preprocessing, inference,

post-processing, and model evaluation.

Model Development & Deployment

Design, train, and optimize deep learning models for vision tasks using modern

architectures.

Work with large datasets to build robust models and continuously improve accuracy

and reliability.

Build production-ready ML systems with monitoring, retraining workflows, and

performance optimization.

Optimize models for real-time inference and edge or cloud deployment.

Productization of Vision AI

Translate computer vision capabilities into scalable enterprise products.

Collaborate with product and engineering teams to integrate vision models into

business workflows and APIs.

Design architectures for high-throughput image/video processing systems.

Technical Leadership

Lead and mentor a team of ML engineers and data scientists working on vision

systems.

Establish best practices for:

model development

dataset creation and labeling

experimentation

evaluation frameworks

Drive technical decisions around model architectures, training infrastructure, and

deployment strategies.

Cross-functional Collaboration

Work closely with product teams, domain experts, and engineering teams to

define use cases and translate them into AI solutions.

Collaborate on data pipelines, infrastructure, and APIs required for vision

products.

Required Qualifications

6+ years of experience in AI/ML with significant work in computer vision systems.

Strong experience building production-grade computer vision models for images

and videos.

Hands-on expertise with modern deep learning frameworks (e.g., PyTorch,

TensorFlow).

Experience implementing and deploying models such as:

CNN-based architectures

transformer-based vision models

object detection frameworks

segmentation models

Experience working with large-scale image or video datasets.

Strong programming skills in Python and experience building ML pipelines.

Preferred Experience

Experience building vision-based enterprise products rather than only research

prototypes.

Experience with:

video analytics systems

inspection systems

visual quality detection

Familiarity with MLOps practices such as experiment tracking, model monitoring,

and automated retraining.

Experience optimizing models for real-time inference or edge deployment.

Experience working in enterprise software or regulated industries.

What We Value

Strong product mindset ability to translate AI capabilities into real-world

applications.

Ability to balance research exploration with production delivery.

Comfort working in fast-moving startup environments.

Strong communication and collaboration skills.