Backend Engineer
at Cloud infrastructure solutions and support company. (SE1)
Python
R Programming- Set up, manage, automate and deploy AI models in development and production infrastructure.
- Orchestrate life cycle management of AI Models
- Create APIs and help business customers put results of the AI models into operations
- Develop MVP ML learning models and prototype applications applying known AI models and verify the problem/solution fit
- Validate the AI Models
- Make model performant (time and space) based on the business needs
- Perform statistical analysis and fine-tuning using test results
- Train and retrain systems when necessary
- Extend existing ML libraries and frameworks
- Processing, cleansing, and verifying the integrity of data used for analysis
- Ensuring that algorithms generate accurate user recommendations/insights/outputs
- Keep abreast with the latest AI tools relevant to our business domain
- Bachelor’s or master's degree in Computer Science, Statistics or related field
- A Master’s degree in data analytics, or similar will be advantageous.
- 3 - 5 years of relevant experience in deploying AI models to production
- Understanding of data structures, data modeling, and software architecture
- Good knowledge of math, probability, statistics, and algorithms
- Ability to write robust code in Python/ R
- Proficiency in using query languages, such as SQL
- Familiarity with machine learning frameworks such as PyTorch, Tensorflow and libraries such as scikit-learn
- Worked with well know machine learning models ( SVM, clustering techniques, forecasting models, Random Forest, etc.)
- Having knowledge in CI/CD for the building and hosting the solutions
- We don’t expect you to be an expert or an AI researcher, but you must be able to take existing models and best practices and adapt them to our environment.
- Adherence to compliance procedures in accordance with regulatory standards, requirements, and policies.
- Ability to work effectively and independently in a fast-paced agile environment with tight deadlines
- A flexible, pragmatic, and collaborative team player with an innate ability to engage with data architects, analysts, and scientists.
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