As a Software Specialist with a focus on AI, Machine Learning (ML), Deep Learning (DL), and Image Processing, you will design, develop, and optimize algorithms and software solutions to process and analyse visual data, such as maps, AutoCAD drawings, and images. Your expertise will drive the identification, measurement, and analysis of objects (e.g., pole heights) and other features within visual data. Working in a focused AI team, you will collaborate closely with data scientists and machine learning engineers to implement cutting-edge AI models into real-world applications, enhancing system capabilities in complex environments.
Educational Qualification:
Any Degree (preferably in IT or related fields).
Years of Experience:
4+ years (up to 10 years) of hands-on experience in Python development.
Key Responsibilities:
Develop and Implement Advanced Algorithms: Create, implement, and test machine learning and deep learning-based image processing algorithms to analyse visual data from maps (e.g., Google Maps), AutoCAD drawings, and other images.
Optimize Performance for Large-Scale Systems: Ensure the developed models and algorithms are optimized for high performance and scalability, leveraging tools and frameworks such as TensorFlow, PyTorch, and OpenCV to process large datasets efficiently.
Integrate AI and ML Models: Work closely with machine learning engineers to integrate AI functionalities such as object detection, image segmentation, and feature extraction into larger software systems, enhancing automation and accuracy in data analysis.
Leverage Pre-Trained Models and Fine-Tuning: Utilize pre-trained models for tasks like image classification, object detection, and semantic segmentation, and finetune them to meet specific project requirements.
Troubleshoot, Debug, and Optimize: Identify and resolve performance bottlenecks, bugs, and inefficiencies in the AI and image processing systems. Apply state-of-the art techniques to improve model accuracy and processing speed.
Collaboration with Cross-Functional Teams: Collaborate with other teams to integrate computer vision solutions with broader systems, focusing on creatingseamless end-to-end solutions.
Documentation and Knowledge Sharing: Document technical solutions, models, and code clearly and thoroughly. Actively share knowledge and best practices with the team to contribute to process improvements and team growth.
Continuous Learning: Keep up-to-date with the latest trends and technologies in AI,ML, deep learning, and image processing to ensure that your solutions are innovative and on the cutting edge.
Proven Experience in AI, ML, and Image Processing: Demonstrated experience in software development with a strong focus on AI, machine learning, deep learning, and image processing, particularly in real-world applications.
Programming Languages: Proficiency in languages like Python, C++, or similar, with experience in libraries and tools for AI and image processing.
Experience with AI & Deep Learning Frameworks: Hands-on experience with AI/ML frameworks and libraries such as TensorFlow, PyTorch, Keras, and OpenCV, specifically for image analysis tasks.
Machine Learning Expertise: Knowledge of supervised and unsupervised learning,neural networks, decision trees, and ensemble methods for solving real-worldchallenges.
Experience with Computer Vision Techniques: Practical knowledge of techniques such as object detection (YOLO, SSD), image segmentation (U-Net, Mask R-CNN),feature extraction, and optical character recognition (OCR).
Strong Analytical and Problem-Solving Skills: Ability to break down complex, real-world problems and design innovative solutions using AI/ML models and image processing techniques.
Team Collaboration: Ability to work effectively within a small, highly focused team, collaborating with machine learning engineers, data scientists, and other technical stakeholders.
Cloud and Big Data Processing: Experience with cloud technologies like AWS, Google Cloud, or Microsoft Azure for deploying and scaling AI solutions.
Good Communication Skills: Ability to clearly document solutions, algorithms, and technical workflows, and share insights with the team for continuous improvement.
Experience with GIS and Mapping Technologies: Familiarity with GIS systems, Google Maps API, or similar map-based data analysis tools.
Knowledge of CAD Systems: Familiarity with AutoCAD or other CAD software for interpreting and analysing technical drawings.
Experience with Reinforcement Learning and Generative Models: Exposure to advanced AI techniques such as reinforcement learning, GANs (Generative Adversarial Networks), or other generative models is a plus.
Familiarity with Edge Computing: Experience in optimizing AI models for deployment on edge devices or low-latency environments is desirable.
Technologies & Tools:
AI/ML Libraries: TensorFlow, PyTorch, Keras, Scikit-learn, Caffe.
Deep Learning Tools: Keras, MXNet, TensorFlow Lite (for edge deployment).
Image Processing Frameworks: OpenCV, Pillow, scikit-image.
Cloud Platforms: AWS (SageMaker, Lambda), Google Cloud (AI & ML tools), Microsoft Azure (ML Studio).
Development Environments: Jupyter Notebooks, Git, Docker, Kubernetes (for containerization and deployment).
Visualization Tools: Matplotlib, Seaborn, TensorBoard for visualizing model performance.
Additional Skills and Traits:
Attention to Detail: Strong focus on ensuring high levels of accuracy in image analysis and object identification tasks.
Passion for Innovation: Interest in continuously experimenting with the latest AI techniques to tackle complex, real-world problems.
Adaptability: Ability to thrive in an agile environment with changing requirements and fast-paced development cycles.
Python, TensorFlow, PyTorch, OpenCV, Scikit-learn, Keras, Deep Learning, Image Processing, Object Detection, Image Segmentation, Feature Extraction, AWS, Google Cloud, Microsoft Azure, Jupyter Notebooks, Git, Docker, Kubernetes, Matplotlib, Seaborn.