Senior AI Engineer – ISP & Vision Systems

The Senior AI / ML Engineer – ISP & Vision Systems will design, develop, and deploy production-grade AI solutions for image signal processing (ISP), computer vision, and vision-based systems. This role focuses on applying machine learning, deep learning, and Gen-AI to improve image quality, defect detection, visual analytics, and vision-driven decision systems.

The role requires close collaboration with ISP, camera, embedded, data analytics, and product teams to translate real-world imaging challenges into scalable AI-powered vision solutions.

Requirements

Key

Responsibilities

AI Strategy &

Vision Use-Case Development

• Collaborate with

cross-functional teams and ISP / vision domain experts to identify

opportunities for AI integration in imaging and vision workflows

• Help AI product

managers and business stakeholders understand the capabilities, limitations,

and trade-offs of AI in ISP and vision systems

• Lead proof-of-concept

(PoCs) and pilot programs to demonstrate measurable value of AI-based vision

solutions

Data Engineering

& Infrastructure

• Analyze, preprocess,

and transform large datasets including images, video streams, metadata, logs,

and sensor data

• Design and build data

ingestion and transformation pipelines for vision datasets

• Set up and manage AI

development and production infrastructure (edge and cloud-based vision systems)

• Collaborate with the

Data Analytics team to integrate large-scale vision data solutions

Model Development

& Deployment

• Design, train, and

deploy AI / ML models for

• Image enhancement and

restoration

• Defect and anomaly

detection

• Object detection,

tracking, and segmentation

• Vision-based analytics

and automation

• Build AI models from

scratch and apply transfer learning for vision tasks

• Identify and curate

new datasets for training and validation

• Deploy models into

production environments, including embedded and edge devices

• Create APIs and

services to integrate AI outputs into downstream vision and business

applications

Monitoring,

Optimization & Adoption

• Monitor AI and vision

system performance and continuously improve deployed models

• Optimize models for

latency, accuracy, and resource constraints on edge devices

• Provide technical

documentation, training, and operational support to engineering and production

teams

Research &

Innovation

• Stay current with

latest AI/ML, computer vision, and ISP advancements

• Propose innovative

AI-driven approaches for ISP pipelines and imaging systems

• Participate in

fast-paced prototyping to explore improvements in vision and imaging workflows

Qualifications & Experience

Minimum

Qualifications

• Bachelor’s degree in

Computer Science, Data Science, Electronics, Imaging, or related field

• Strong programming

skills in Python, C/C++, R, VB.NET, or equivalent

• Proficiency with ML

frameworks such as TensorFlow, PyTorch, scikit-learn

• Hands-on experience in

AI vision, computer vision, or Gen-AI

• Experience in

Full-Stack Development

• Experience deploying

ML models in production vision systems (edge or cloud)

• Strong analytical,

problem-solving, and cross-functional collaboration skills

• Excellent verbal and

written communication skills

Preferred

Qualifications

• Strong knowledge of

ISP pipelines including demosaicing, noise reduction, HDR, color correction,

and sharpening

• Experience with deep

learning for image classification, segmentation, and object detection

• Knowledge of LLMs,

Prompt Engineering, RAG, and LLM fine-tuning

• Experience with model

explainability, validation, and performance benchmarking in vision systems

• Familiarity with

camera sensors, optics, and imaging hardware

• Experience working

with image/video datasets, metadata, and vision telemetry

Added Advantage

• Knowledge of ISP

tuning, camera calibration, and image quality evaluation

• Understanding of

sensors, drivers, Video and camera control pipelines

• Experience with data

processing and streaming tools

• Kafka
• Spark
• Apache NiFi
• Dataiku
• Familiarity with NoSQL

and distributed storage systems

• Cassandra
• MongoDB
• HDFS
• Experience with

software engineering tools

• JIRA
• Jenkins
• Git
• Confluence
• Strong foundation in

algorithms and data structures