01Overview
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
quick -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 .
