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Computer Vision Engineer
Currently, We are looking for Computer Vision Engineers to help us create artificial intelligence products along with our AI Team. Computer Vision Engineer responsibilities include creating machine learning models and retraining systems. To do this job successfully, you need exceptional skills in statistics and programming. If you also have knowledge of data science and software engineering, we’d like to meet you.
Your ultimate goal will be to shape and build efficient self-learning applications and to deliver high-quality services that are aligned with our clients’ needs and business goals.
Duties and Responsibilities
The Computer Vision Engineer will be responsible for delivering scalable, low latency, and high-performance Machine Learning solutions for different product use-cases.Create language models from text data. These language models draw heavily from Statistical, Deep Learning as well as rule-based research in recent times.
• Design and implement state-of-the-art Machine Learning approaches.
• Build end-to-end Machine Learning pipelines, including stages such as data pre-processing, model generation, cross-validation, and active feedback.
• Take ownership for designing, implementing, monitoring, and maintaining the Machine Learning modules that power the way forward platform.
• Work in a highly collaborative environment with other experts in Machine Learning & Data Science, Engineering, and DevOps teams.
• Work Closely with the Data Sampling Team for Appropriate Dataset Collection
• Work on Challenging Problem Statements to fine-tune models with Huge Dataset
• Implementation of the SOTA Architectures for Model Training
• Work Actively as a part of the Computer Vision and Deep Learning Team to Train Computer Vision Models
• To have innovative design strategies and plans in an endeavor to develop high quality work in favor of the organization.
• Provide internal support for all assigned projects and assist various team members for the enhancement of projects.
• Require keeping self-updated by learning the new programming languages, which will enhance the performance of the business of the company with a positive approach.
• An Engineering graduate with Machine Learning or Computer Science or Information Technology stream.
• 1-5 years of work or equivalent hands-on experience with design & implementation of Machine Learning models for solving business problems.
• Should have strong programming skills in Python in building Machine Learning models using libraries such as numpy, pandas, scikit-learn, nltk, etc.
• Hands-on experience with Deep Learning libraries like Pytorch, Tensorflow, or Keras and Databases (SQL and/or NoSQL).
• Experience or hands-on experience in applying different Natural Language Processing techniques to problems such as Text Classification, Text Summarization, Sentiment Analysis, Information Retrieval, Knowledge Extraction, and Conversational AI designs - potentially with both traditional & Deep Learning techniques.
• Experience or hands-on experience in choosing/coming up with, implementing, and fine-tuning Supervised and Unsupervised algorithms for a given problem based on standard feature selection, model selection process, and validation approaches.
• Should possess strong problem-solving, presentation & communication skills.
• Ability to effectively work within and contribute to a collaborative team environment.
• Should be highly motivated, organized, proactive self-starter, learner, and a team player. Takes ownership and makes the required judgments whenever necessary.
• Experience in building containerized ML applications/micro services using Docker and (or) AWS is a Plus.
• Experience in Continuous Integration and Delivery/Development (CI/CD), Git is a Plus.
• Proficient with Training of Detection/Classification/Segmentation Models with Tensorflow/PyTorch
• Good understanding of Dataset Quality for Computer Vision Applications.
• Strong understanding of Model Training Dynamics. Should be able to find out the error and resolve it based on training/eval metrics.
• Good Theoretical and Practical Knowledge with the fundamentals of Deep Learning, eg. CNNs, Regularization Techniques, etc.
• Familiarity with State-of-the-Art Models like YOLO-series, Efficient Net/EfficientDet, etc.
• Experience with using Docker containers for Computer Vision/Deep Learning.
If you're interested in applying to the stated position, kindly share your updated resume.