Architect (AI)

August 24, 2024

bebo Technologies

bebo Technologies is a leading complete software solution provider. bebo stands for 'be extension be offshore'. We are a business partner of QASource, inc. USA[www.QASource.com]. We offer outstanding services in the areas of software development, sustenance engineering, quality assurance and product support. bebo is dedicated to provide high-caliber offshore software services and solutions.
Our goal is to 'Deliver in time-every time'.

For more details visit our website: www.bebotechnologies.com

Let's have a 360 tour of our bebo premises by clicking on below link:

https://www.youtube.com/watch?v=S1Bgm07d Pm MKey

Key Responsibilities:

Lead AI/ML Algorithm Development: Design, develop, and optimize machine learning algorithms.
Architect High-Performance AI Solutions: Architect and implement high-performance software solutions for AI applications, focusing on scalability, efficiency, and accuracy.
Drive Innovation: Stay current with the latest advancements in AI frameworks, flows, and compilation techniques. Contribute actively to developing best practices.
Develop Scalable Solutions: Design and develop scalable AI and machine learning solutions using deep learning frameworks such as Tensor Flow, Py Torch, or Keras.
Natural Language Processing Expertise: Utilize NLP techniques and frameworks (e.g., NLTK, Spa Cy) for text analytics and sentiment analysis.
Computer Vision Applications: Apply hands-on experience with computer vision frameworks (e.g., Open CV, Tensor Flow Object Detection API) for image and video processing tasks.
Cloud Deployment: Deploy machine learning models in cloud environments using platforms such as:

AWS: Sage Maker
Google Cloud: AI Platform, Vertex AI
Azure: Azure Machine Learning
IBM: Watson Machine Learning
Alibaba Cloud: Machine Learning Platform for AI (PAI)
Oracle Cloud: Data Science
H2O.ai: H2O Driverless AI
Data Robot: Automated Machine Learning (Auto ML)
Google Colab: For collaborative development

Reinforcement Learning: Implement reinforcement learning algorithms and apply them to real-world scenarios.
Data Pipelines and ETL: Implement and optimize data pipelines and ETL processes for machine learning workflows using tools such as Apache Airflow or equivalent.
Model Evaluation: Use performance evaluation metrics and techniques for model selection and tuning.
Containerization: Utilize containerization technologies (Docker, Kubernetes) for packaging and deploying AI/ML applications.
Dev Ops and CI/CD: Implement Dev Ops practices and CI/CD pipelines for automated testing and deployment of machine learning models.
Programming Proficiency: Code in languages such as Python, R, Java, or Scala for building and maintaining AI/ML applications.
Software Engineering Principles: Apply strong software engineering principles, including design patterns, data structures, and algorithm optimization.
Collaborate with Teams: Work effectively with cross-functional teams including data scientists, software engineers, and business stakeholders.
Problem-Solving: Exhibit excellent problem-solving skills and troubleshoot complex issues in production environments.
Stay Updated: Keep up with the latest trends and advancements in AI/ML technologies and research, and apply them to enhance product capabilities.
Sales and Marketing Collaboration: Collaborate with sales and marketing teams to develop AI/ML service offerings, proposals, and presentations for prospective clients.
Pre-Sales Activities: Participate in pre-sales activities, including customer meetings, workshops, and demonstrations to showcase our AI/ML capabilities.

Technical Skills:

Proficiency in machine learning frameworks and libraries such as Tensor Flow, Py Torch, Keras, and Scikit-learn.
Strong expertise in Direct ML and other AI/ML frameworks.
In-depth knowledge of NLP techniques and frameworks like NLTK and Spa Cy.
Experience with computer vision frameworks like Open CV and Tensor Flow Object Detection API.
Proficiency in cloud platforms for deploying ML models like AWS Sage Maker, Google AI Platform, Vertex AI, Azure Machine Learning, IBM Watson Machine Learning, Alibaba Cloud PAI, Oracle
Cloud Data Science, H2O Driverless AI, Data Robot Auto ML, and collaborative tools like Google Colab.
Strong understanding of reinforcement learning algorithms.
Experience with data pipeline and ETL tools such as Apache Airflow.
Knowledge of containerization technologies like Docker and Kubernetes.
Familiarity with Dev Ops practices and CI/CD pipelines.
Strong programming skills in Python, R, Java, or Scala.
Deep understanding of software engineering principles.
Collaboration and Communication: Excellent communication and teamwork skills, with the ability to collaborate effectively with cross-functional teams.
Problem-Solving: Strong problem-solving skills and the ability to troubleshoot complex issues in production environments.
Innovation and Learning: A proactive approach to staying updated with the latest AI/ML trends and incorporating them into solutions.
Sales Support: Ability to support sales and marketing efforts, including pre-sales activities and client presentations.