What are the responsibilities and job description for the AI/ML Engineer II position at reflexiveconcepts?
Reflexive Concepts is seeking a skilled Artificial Intelligence/Machine Learning Engineer to join our team!
Specifically, we are looking for an AI/ML Engineer to design, create, test, and productize AI/ML algorithms in support of the FFPP VISTA Development and Sustainment (MT&E Team) mission. The AI/ML Engineer should be proficient in model architecture, data pipeline interaction, and metrics application, and familiar with application development, infrastructure management, data engineering, and data governance.
Qualifications:
Specifically, we are looking for an AI/ML Engineer to design, create, test, and productize AI/ML algorithms in support of the FFPP VISTA Development and Sustainment (MT&E Team) mission. The AI/ML Engineer should be proficient in model architecture, data pipeline interaction, and metrics application, and familiar with application development, infrastructure management, data engineering, and data governance.
Qualifications:
- Five (5) years experience in applied machine learning in programs and contracts of similar scope, type, and complexity
- A Master’s or Ph.D. degree in advanced math, artificial intelligence, data science, computer science or deep learning from an accredited college or university
- 5 additional years of machine learning experience with a relevant Bachelor’s degree may be substituted for a Master’s degree
- Experience with standard machine learning frameworks, e.g. PyTorch, TensorFlow
- Select appropriate data sets
- Perform statistical analysis
- Run machine learning algorithms
- Use results to improve models
- Train and retrain systems when needed
- Experience 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