Computer Vision Specialist
Roles and responsibilities
Code, train, evaluate and deploy machine learning models that integrate with the complete software solution.
B Tech in Computer Science / Information Technology
M Tech / PhD specialization in computer vision
Candidate must be good at programming and be able to adapt to any of the basic programming languages like C, C++, Python, Matlab, R, Julia, Java, Go, Rust etc.
Candidate must have mastery of basic computer science concepts like data structures, algorithms, databases, relational algebra (SQL), operating systems, computer architecture, computer networks.
Candidate must be comfortable in programming on GNU/Linux in a high performance computing (HPC) setups like multicores, clusters, GPUs, etc. Candidate must be able to grasp concepts from latest research papers and implement them in a short time.
Candidate must have a specialization in AI / ML and should have mastery over the topics in the prerequisites section.
Candidate must be familiar with ML programming frameworks and libraries and should be able to quickly learn and adapt to the newly emerging ones.
Candidate must be familiar with computer vision libraries, frameworks, toolboxes and should be able to quickly learn and adapt to the emerging ones.
A) Computer Vision
1. Computer Vision: A Modern Approach | Forsyth, Ponce
2. Programming Computer Vision with Python | Jan Erik Solem
3. Computer Vision: Algorithms and Applications | Richard Szeliski
4. Computer Vision: Models, Learning, and Inference | Simon J. D. Prince
5. Multiple View Geometry in Computer Vision | Hartley, Zisserman
B) Deep learning for computer vision Pre-requisites for Machine Learning Engineer.
Following certifications will be preferred
Introduction to Computer Vision, Udacity | GeorgiaTech CS 6476 | Prof. Aaron Bobick, Prof Irfan Essa, Arpan Chakraborty
Swayam | Deep Learning for Computer Vision By Prof. Vineeth N Balasubramanian | IIT Hyderabad
CVIT Summer School series | IIIT Hyderabad Tools A computer vision specialist needs to be proficient in different aspects of computer science and engineering.
Some of the tools to be familiar with include:
Python, Julia, R, jupyter, Pandas, PySpark, numpy, matplotlib, seaborn, streamlit, Kafka
Core Computer Science
C, C++, Python,Java, Scala, NetworkX, igraph, MySQL, PostgreSQL, Linux, Mac, Windows
PyTorch, Tensorflow, Keras, scikit-learn, XGBoost, LightGBM
OpenCV, dlib. scikit-image, SpaCy, faiss, flann, kaldi, sphinx, librosa Systems / Computing
OpenMP, MPI, Spark, CUDA, AWS, GCloud, Azure, Mosquitto, Paho, Jetson Nano
Docker, Git, JIRA, Trello, MLOps toolkits