What are the responsibilities and job description for the AI Engineer position at Avid Federal Contracting?
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.
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.