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Principal ML Analytics Engineer
Location
Ireland
Posted
7 days ago
Salary
0
Seniority
Lead
Job Description
Principal ML Analytics Engineer
Sun Life
• Design, develop, install, deploy, test, medium-scale analytics models and AI applications in support of business and client outcomes and objectives. • Design and implement training pipelines for machine learning models. • Build leading MLOps pipelines to deploy and update machine learning models and analytics applications. • Build leading feature engineering pipelines extracting and preparing data for machine learning model performance. • Train and evaluate models using diverse datasets and performance metrics, choosing and applying appropriate machine learning algorithms. • Maintain and optimize model to improve accuracy and monitor reliability. • Implement leading monitoring mechanisms to track model performance and identify issues. • Detect and mitigate biases in machine learning models to promote fairness. • Write leading specifications of machine learning models and processes for reproducibility and maintenance. • Present insights and performance tuning recommendations from machine learning models to inform business decisions. • Promote ethical principles and responsible AI guidelines for machine learning development. • Integrate machine learning models into production environments. • Develop and deploy APIs for model access and prediction requests. • Leverage best practices and patterns in MLOps (machine learning operations) to ensure efficient and scalable model lifecycle management. • Integrate leading AI service and machine learning models with existing applications and DevSecOps deployment pipelines. • Embrace Agile development practices and employ SDLC processes on all development. • Collaborate with data scientists practitioners, finance and business analysts, actuaries and engineers to achieve model objectives. • Effectively mentor other engineers. • Lead a "data-first" approach to problem solving and designing IT solutions.
Job Requirements
- Bachelor’s degree in a computing related discipline or equivalent industry experience is required.
- 7-10 years of industry experience is preferable with roles that demonstrate senior levels of software engineering and data analytics responsibilities.
- Leading understanding of artificial intelligence, generative AI, and data science concepts and principles, including machine learning algorithms, statistical methods, and data mining technique.
- Proven proficiency in various platforms managing the life cycle of machine learning models, practices and tools involved in managing the machine learning lifecycle (MLOps), such as model versioning, monitoring, and deployment.
- Leading knowledge of data science platforms, big data technologies.
- Understanding of AI and ML Service Platforms like Dataiku, Amazon SageMaker or Azure Machine Learning.
- Leading skills in mathematical / data science programming languages, particularly Python, R, and SQL.
- Ability to utilize these languages for data exploration, manipulation, and analysis.
- Proficiency in database management using relational and non-relational databases, Cloud data warehousing platforms with multi-clustered distributed data architecture highly scalable, and elastic compute.
- Strong understanding of data streaming technologies and event-driven architectures.
- Proven ability to continuously improve machine learning models through data monitoring, retraining, and adaptation to changing data patterns.
- Leading deployment expertise in managing the life cycle of AI and machine learning model from analytics sandboxes all the way to deploying machine learning models into production environments and effectively monitoring their performance.
- Leading ability to design, develop and maintain AI and machine learning systems, leveraging best practices in MLOps principles.
- Deep understanding of bias detection techniques to ensure ethical and unbiased machine learning models.
- Deep understanding of data & model governance, Bias detection and ethical AI.
- Demonstrate knowledge of data governance principles, including data quality, security, and compliance.
- Proven track record of designing, developing, and maintaining machine learning and AI applications, encompassing data preparation, model training, evaluation, and deployment.
- Strong knowledge of agile DataOps, MLOps and AIOps methodologies, enabling rapid development, testing, and deployment of machine learning solutions.
- Strong understanding of API Integration, and authentication for machine learning models and analytics applications.
Benefits
- Flexible work arrangements
- Professional development opportunities
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