Artificial Intelligence Engineer
3 weeks ago
POSITION SUMMARY AND PRIMARY RESPONSIBILITIES :
Position Summary (the reasons the position exists; a summary of what the is position is responsible for) :
- The AI Engineer develops capabilities required to construct personalized adaptive learning technologies that enable effective, efficient, and engaging learning (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, Train and retrain machine learning models and provide metrics to document and track 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 dat 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 an 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 issue 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.
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.
(ref:hirist.tech)
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