AI Architect

Role: AI Architect
Work location: Dallas, TX 75201
Duration: 8 Months
Job Description:
Job Summary.
Good to have AI Architect is a technical leader responsible for designing, building, and maintaining scalable, high-performing machine learning (ML) systems. This role bridges the gap between business needs and technical feasibility, ensuring ML initiatives align with overall company strategy.
Responsibilities:

• Design and develop the architecture for ML platforms and solutions.
• Select and implement appropriate ML algorithms and tools based on business requirements and data characteristics.
• Collaborate with data scientists, data engineers, and software engineers to ensure successful integration of ML models into production systems.
• Oversee data pipelines and ensure data quality for training and deploying ML models.
• Monitor and evaluate the performance of ML models, identifying opportunities for improvement and retraining.
• Stay up-to-date on the latest advancements in ML research and best practices.
• Develop and implement strategies for managing model bias and ensuring ethical use of AI.
• Communicate complex technical concepts to both technical and non-technical stakeholders.
• Mentor and guide junior team members on ML architecture best practices.

Qualifications:

• Master's degree in Computer Science, Engineering, or a related field (or equivalent experience).
• Strong understanding of machine learning concepts, algorithms, and frameworks.
• Experience designing and developing scalable, distributed systems.
• Experience with cloud platforms (AWS, Azure, Google Cloud Platform) is a plus.
• Excellent communication and collaboration skills.
• Proven ability to translate business goals into technical requirements.
• Experience working in an Agile development environment is a plus.

Tools and Technologies (possible, depending on the company):

• Python (Scikit-learn, TensorFlow, PyTorch).
• Machine Learning Platforms (AWS SageMaker, Azure Machine Learning, Google AI Platform).
• Cloud Computing Technologies (AWS, Azure, Google Cloud Platform).
• Docker, Kubernetes.
• DevOps Tools (Git, Jenkins, CI/CD pipelines).