Machine Learning Engineer

Please, no third parties. Permanent residents only.
This is a direct-hire or contract-to-hire opportunity.


Main Duties & Responsibilities:
- Design, construct, and fine-tune machine learning models using Python, Nvidia frameworks, and other relevant technologies to tackle diverse challenges such as risk evaluation and customer profiling.
- Apply advanced techniques in feature engineering, hyperparameter tuning, and performance optimization to enhance model effectiveness.
- Work alongside the team to establish CI/CD workflows for seamless deployment of ML models within Azure cloud infrastructure.
- Manage the full model lifecycle, incorporating version control, containerization, and automated deployment methodologies.
- Collaborate with Data Engineers to develop well-structured, high-quality data pipelines for model training and evaluation.
- Utilize SQL and other database tools to preprocess, clean, and structure data sets for analysis.
- Implement robust monitoring systems to track model accuracy, performance, and potential data drift.
- Diagnose issues in deployed models and optimize them to maintain consistency and reliability.
- Engage with actuarial teams, underwriting experts, and business stakeholders to translate business objectives into AI-driven solutions.
- Contribute to Agile development processes, including sprint planning, daily stand-ups, and retrospective discussions.
- Maintain comprehensive documentation covering model design, data workflows, and deployment protocols.
- Effectively convey insights and project updates to both technical and non-technical audiences.

Skills & Requirements:
- Bachelors Degree or higher in Computer Science, Data Science, Engineering, or a related field is highly preferred.
- 3+ years of hands-on experience in machine learning within a production setting.
- Strong proficiency in Python and deep learning frameworks such as TensorFlow and PyTorch.
- Hands-on experience with Azure or other cloud platforms (AWS, Google Cloud Platform) for deploying machine learning models.
- Knowledge of SQL, NoSQL, or similar database technologies.
- Understanding of MLOps best practices, including CI/CD automation and containerization (Docker).
- Familiarity with Agile development practices, including Scrum methodologies.
- Experience with version control systems like Git and automated testing frameworks.
- Strong communication skills with the ability to collaborate effectively in team environments.
- Analytical mindset with a keen attention to detail and problem-solving abilities.
- Knowledge of ETL processes and data pipeline optimizations. Experience with distributed computing frameworks like Apache Spark.



tags: senior machine learning engineer, senior ml engineer, senior data engineer, senior data analyst, senior business intelligence engineer, senior bi engineer, senior mlops engineer, senior cloud data engineer