AI Machine Learning Skill 3-FFPP-8953

The Artificial Intelligence/Machine Learning (AI/ML) Engineer designs creates tests and productizes AI/ML algorithms to solve business challenges. The AI/ML models they create should be capable of learning and making predictions as defined by the business logic developed to meet customer requirements. The AI/ML Engineer should be proficient in all aspects of model architecture data pipeline interaction and metrics application interpretation and presentation. The AI/ML Engineer needs familiarity with foundational concepts of application development infrastructure management data engineering and data governance. Through an understanding of training retraining deploying scheduling monitoring and improving models through iterative user and system feedback the AI/ML Engineer designs and creates scalable solutions for optimal performance. The AI/ML Engineer may be responsible for leading geographically diverse teams and will often serve as a primary POC for AI-related matters so must have exceptional analytical problem-solving and communication skills. Expert knowledge of multiple programming languages e.g. Python Java C R a plus.

10 years experience deploying machine learning algorithms is required. A Masters or Ph.D. degree in advanced math artificial intelligence data science computer science or deep learning from an accredited college or university is required. 7 additional years machine learning experience with a relevant Bachelors degree may be substituted for a Masters degree. Demonstrated abilities in software engineering and AI/ML model test and evaluation.
Select appropriate data sets
Perform statistical analysis
Run machine learning algorithms
Use results to improve models
Train and retrain systems when needed
Experience in working with various ML libraries and packages
Run standard test and evaluation protocols
Provide system integration oversight
Oversee Test and evaluation of AI and ML algorithms through an iterative design process to meet verification and validation requirements
Research and implement a broad range of AI and ML algorithms and tools
Design or Select appropriate data and knowledge representation methods
Recognize software architecture data modelling and data structures
Transform and convert data science prototypes into scalable solutions
Verify data and model output quality
Identify differences in data distribution that affect model performance
Develop criteria for test and evaluation to include explainability and resiliency
Deep understanding of AI logic semantics ontologies and knowledge representation
Demonstrated ability to design evaluate and productize AI/ML models on a range of commercial cloud-based architectures
Design ML algorithms according to customer requirements
Research experiment with and implement suitable ML algorithms and tools
Broaden current AIML frameworks and machine learning libraries.