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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 - Bachelors degree in Computer Science, Data Science, Electronics, Imaging, or related field - Solid 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 .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.