AI Engineer

Description

A day in the life:

We're looking for an AI Engineer to work closely with system and software engineers to design, prototype, and integrate AI advancements into the existing RAD architecture. This is a transformative position focused on building solutions that augment human intelligence, streamline workflows, and enhance the overall effectiveness of intelligence operations.

What you bring to the table:

• 10 + years of relevant experience with AI, Data Science and Development
• A Bachelor of Science degree in Computer Science or a related field from an accredited institution.  Four (4) addn'l years of relevant experience may be considered in lieu of a degree
• 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 non-technical 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.

The total package: