- Flexible schedule
- Help or transport service
- Training & development
AI Architect (Hybrid - Richardson, Texas)
General Information
Description: The AI Architect is responsible for developing and implementing artificial
intelligence solutions to solve complex problems and enhance business operations. This role
works closely with cross-functional teams to design, develop, and deploy AI models and
algorithms that enable data-driven decision making. The AI Architect is an expert in machine
learning, deep learning, and data analysis and creates intelligent systems that automate
processes, improve efficiency, and drive business innovation.
Target Start Date – Feb 1, 2024
Schedule – 8:00 AM to 5:00 PM CST
Duration – 6 Months
Responsibilities
- Collaborating with cross-functional teams to define AI project requirements and
- Conducting research to stay up-to-date with the latest advancements in generative AI,
them into our products and services.
- Optimizing existing generative AI models for improved performance, scalability, and
- Creates, implements, and deploys AI solutions in business environments, considering
- Assists in collecting and cleaning relevant datasets to ensure their suitability for
- Performs thorough testing and evaluation of AI models to ensure their performance
- Continuously monitors and optimizes the performance of deployed AI systems, making
- Stays updated with the latest advancements in AI and ML technologies to identify their
- Collaborates with data scientists, software engineers, and other stakeholders to
Job Requirements
Education
- A bachelor's degree in computer science, data science, software engineering, or related
Experience
- At least five years of experience with AI data science, ML engineering, or data analytics
- At least two years of experience designing and implementing AI solutions with a focus
mining
- Experience on projects involving big data processing and distributed computing
- Experience with Azure Open AI Models, Amazon Bedrock/SageMaker/Q, Microsoft CoPilot, etc
Skills
- Expert knowledge of machine learning techniques, including supervised and
- Proficiency in programming languages such as Python, R, JavaScript, and C++
- Knowledge of popular machine learning frameworks such as TensorFlow and PyTorch
- Familiarity with cloud platforms, such as AWS, Azure and GCP, and their relevant AI
- Familiarity with data engineering concepts, including data pipelines, data integration,
- Ability to work with large datasets and perform data cleaning, transformation, and
- Strong analytical and creative problem-solving skills
- Ability to effectively communicate technical information to business executives
- Adaptability and willingness to learn new AI technologies and techniques