AI Engineer

Design and Development: Lead the design and development of AI-driven solutions from

conception to deployment, ensuring seamless integration with the existing software

architecture. This includes prototyping new models, writing production-quality code,

and maintaining existing AI systems.

Collaboration and Communication: Serve as a key technical liaison, collaborating with

cross-functional teams including system engineers, software developers, and domain

experts. Effectively present and articulate recommended AI approaches, discussing the

tradeoffs and implications of different implementations with both technical and nontechnical

stakeholders.

Data Analysis and Modeling: Conduct Exploratory Data Analysis (EDA) on diverse

datasets (both structured and unstructured) to inform the data model, identify data

quality issues, and determine optimal input formats for AI models.

Model Building and Evaluation: Develop, train, and evaluate a variety of machine

learning models, ensuring they meet performance and reliability requirements.

Implement robust testing and validation strategies to ensure models are accurate and

unbiased.

Continuous Improvement: Stay current with the latest advancements in AI and machine

learning, continuously seeking opportunities to apply new technologies and

methodologies to improve existing systems and solve complex problems.

Cloud Platforms: Have experience with the Amazon Web Services (AWS) cloud

computing platform and machine learning operations (MLOps) tools for deploying and

managing machine learning models at scale.