AI Engineer

Mississauga - Canada

Monthly Salary: Not Disclosed

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

Responsibilities:

Lead the end-to-end technical development deployment monitoring and maintenance of real-world production-level Generative AI solutions within a financial context.
Apply advanced NLP techniques to financial data refining prompt engineering strategies for Large Language Models (LLMs) to achieve optimal performance and desired outcomes.
Collaborate extensively with business stakeholders to translate complex business needs into robust and scalable GenAI technical requirements and solutions.
Develop test and maintain high-quality Python code for GenAI applications integrating with various data sources APIs and vector databases.
Design and implement scalable API architectures for GenAI applications ensuring seamless integration and efficient data flow.
Proactively troubleshoot and debug GenAI models in production environments quickly identifying and resolving issues to maintain system stability and performance.
Monitor and optimize MLOps pipelines for GenAI models ensuring efficient training deployment and continuous integration/continuous delivery (CI/CD).
Stay rigorously up to date with the rapidly evolving Generative AI landscape continuously researching and evaluating new tools techniques LLM architectures and emerging technologies.
Participate actively in team meetings contributing to strategic discussions technical design reviews and knowledge sharing sessions.
Communicate complex technical concepts and GenAI capabilities clearly and effectively to non-technical stakeholders translating technical jargon into understandable business terms.
Ensure adherence to best practices in MLOps model governance data privacy and responsible AI principles throughout the development lifecycle.
Expert-level Python programming skills are mandatory.
This includes deep familiarity with core Python as well as extensive proficiency in key libraries for AI/ML and GenAI applications:
Data Structures: Lists dictionaries sets etc.
Scientific Computing: NumPy Pandas SciPy.
Machine Learning: Scikit-learn XGBoost LightGBM.
Deep Learning: TensorFlow PyTorch.
Generative AI specific Libraries: Transformers LangChain

Responsibilities: Lead the end-to-end technical development deployment monitoring and maintenance of real-world production-level Generative AI solutions within a financial context. Apply advanced NLP techniques to financial data refining prompt engineering strategies for Large Language Models (LLMs...

Responsibilities:

Lead the end-to-end technical development deployment monitoring and maintenance of real-world production-level Generative AI solutions within a financial context.
Apply advanced NLP techniques to financial data refining prompt engineering strategies for Large Language Models (LLMs) to achieve optimal performance and desired outcomes.
Collaborate extensively with business stakeholders to translate complex business needs into robust and scalable GenAI technical requirements and solutions.
Develop test and maintain high-quality Python code for GenAI applications integrating with various data sources APIs and vector databases.
Design and implement scalable API architectures for GenAI applications ensuring seamless integration and efficient data flow.
Proactively troubleshoot and debug GenAI models in production environments quickly identifying and resolving issues to maintain system stability and performance.
Monitor and optimize MLOps pipelines for GenAI models ensuring efficient training deployment and continuous integration/continuous delivery (CI/CD).
Stay rigorously up to date with the rapidly evolving Generative AI landscape continuously researching and evaluating new tools techniques LLM architectures and emerging technologies.
Participate actively in team meetings contributing to strategic discussions technical design reviews and knowledge sharing sessions.
Communicate complex technical concepts and GenAI capabilities clearly and effectively to non-technical stakeholders translating technical jargon into understandable business terms.
Ensure adherence to best practices in MLOps model governance data privacy and responsible AI principles throughout the development lifecycle.
Expert-level Python programming skills are mandatory.
This includes deep familiarity with core Python as well as extensive proficiency in key libraries for AI/ML and GenAI applications:
Data Structures: Lists dictionaries sets etc.
Scientific Computing: NumPy Pandas SciPy.
Machine Learning: Scikit-learn XGBoost LightGBM.
Deep Learning: TensorFlow PyTorch.
Generative AI specific Libraries: Transformers LangChain