Consultant - AI Engineer

Position Summary And Primary Responsibilities

Position Summary

Responsibilities(indicate 5-10 key responsibilities/tasks that effectively describe the position; List from most important to least important):

• Adhere to ethical standards and comply with the laws and regulations applicable to the job function.
• Developing AI/ML models to achieve objectives as outlined by Senior or Principal AI Engineers
• Work with data scientists to build data ingest and data transformation infrastructure.
• Implement effective methods of AI/ML model testing during development, deployment, and recalibration.
• Train and retrain machine learning models and provide metrics to document and track their performance.
• Explore the data and identify differences in data distribution that could impact performance when deployed in prototypes.
• Utilize best practices around design, coding, automated unit, regression testing, and deployment of AI/ML models to production.
• Keep current of latest AI research in the personalized learning and assessment field.
• Utilize best practices in the responsible use of AI.
• Work closely with learning scientists and data scientists to define collection events and data transformations needed to drive personalized learning.
• Work closely with Product Owners to understand potentials and limitations of AI/ML in the product.
• Work effectively in the agile-at-scale framework.
• Clearly communicate findings to senior leadership
  

Skills Required

• Experience with Python-based AI/ML frameworks, Java, and/or R
• Experience with ML frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
• Knowledge and practical application of statistical analysis and mathematical modeling concepts and principles
• AI/ML engineers will make heavy use of AWS, Microsoft Azure, OpenAI and Hugging Face models.
• Experience with cloud-based (AWS) deployment of models, performance monitoring and issues troubleshooting.
• Ability to work effectively in a cross-functional team Familiarity with Docker and Git
• Familiarity with Docker and Git
• Excellent written and oral communication skills
• Excellent problem-solving mind set.
• Master's degree in computer science, Applied Mathematics, Engineering, or any related field.
• Min 3 years of Training/experience in AI/ML algorithm development
• Experienced in Technology enhanced learning solution research and development.
  

Mandatory Requirement

• AI Models: Experience in working with OpenAI and Hugging Face models.
• ML Ops and Cloud: Proficiency in cloud-hosted environments (e.g., AWS, Azure) and ML Ops offerings (e.g., AWS Sagemaker, Azure ML)
• ML Lifecycle and Deep Learning: Training, Test, Scoring, Switching, Inference, Evaluation, Data split, Data drift, Product ionization of models, Scalability and Optimization.
• Python and SQL: Evaluate Coding Test & Coding Standards (Mandatory) (Python) (ordereddict, tuples) and SQL.
• Jupyter: Data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning algorithms.
  

Good To Have Requirement

• Methodology: Good exposure to Agile & Scrum Methodologies, GIT, Confluence.
• AI Research: Stay current with the latest AI research in the personalized learning and assessment field.
• Java and/or R: Familiarity with Java, and R code writing is a plus.
• Statistics skills: Advanced Math and Statistics skills (linear algebra, calculus, Bayesian statistics, mean, median, variance, etc.)
• Best Practices: In-depth knowledge of best practices in design, coding, automated unit, regression testing, and deployment of AI/ML models to production.