Pune, Maharashtra, India
Space Exploration & Research, Information Technology
Full-Time
Firstsource
Overview
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 experiences.
Responsibilities (indicate 5-10 key responsibilities/tasks that effectively describe the position ; List from most important to least important):
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 experiences.
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
- 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.
- 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.
- 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.
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