Machine Learning Engineer
About The Position
The Machine Learning Engineer sits on the Data Science & Machine Learning Team to help the CMG (Commercial, Medical and Government) organization achieve its vision by unlocking value from data quicker and more effectively. The Machine Learning Engineer will bring a robust understanding of machine learning operations (MLOps) to manage and contribute to our machine learning initiatives. The Machine Learning Engineer will design, enhance, scale, and maintain machine learning solutions from conception to deployment in a production environment. You will collaborate with data scientists, software engineers, and business stakeholders to translate business requirements into scalable ML systems. You will also architect and uphold ML pipelines using job scheduling frameworks to streamline data preparation, training, deployment, and machine learning model lifecycles. In addition to ensuring best practices in code quality, version control, and CI/CD for machine learning pipelines, you will also design and implement robust monitoring systems for deployed models to track performance, data drift, and anomalies. The ML Engineer will also pioneer and administer model retraining, A/B testing, and progressive deployment strategies for continuous model enhancement. You will contribute to the company's machine learning architecture to support scalable and repeatable model training and deployment. Facilitate the creation of automated processes for model validation and testing.
Requirements
- Bachelor’s Degree in Computer Science or related technical discipline.
- 3+ years of experience working in a machine learning engineer role.
- Minimum of 2 years in an MLOps-focused position.
- Expertise in ML frameworks (e.g., TensorFlow, PyTorch).
- Expertise in programming languages (e.g., Python, Scala, Java).
- Expertise in MLOps technologies (e.g., Kubeflow, MLflow, AWS Sagemaker).
- Expertise in job scheduling frameworks (e.g., Apache Airflow, AWS Step Functions).
- Solid understanding and experience with cloud services and containerization technologies/platforms, particularly AWS and Kubernetes.
- Proficient with software engineering best practices, including agile development, code reviews, SCM, build processes, testing, and operations.
- Experience with distributed computing and big data technologies (e.g., Hadoop, Spark).
Nice To Haves
- Experience building and optimizing structured and unstructured big data pipelines, architectures, and datasets.
- Excellent communication skills to effectively collaborate with cross-functional teams.
Responsibilities
- Manage and contribute to machine learning initiatives with a robust understanding of MLOps.
- Design, enhance, scale, and maintain machine learning solutions from conception to deployment in a production environment.
- Collaborate with data scientists, software engineers, and business stakeholders to translate business requirements into scalable ML systems.
- Architect and uphold ML pipelines using job scheduling frameworks to streamline data preparation, training, deployment, and machine learning model lifecycles.
- Ensure best practices in code quality, version control, and CI/CD for machine learning pipelines.
- Design and implement robust monitoring systems for deployed models to track performance, data drift, and anomalies.
- Pioneer and administer model retraining, A/B testing, and progressive deployment strategies for continuous model enhancement.
- Contribute to the company's machine learning architecture to support scalable and repeatable model training and deployment.
- Facilitate the creation of automated processes for model validation and testing.
Benefits
- Discretionary annual bonus may be available based on individual and Company performance.
- Benefits detailed at the link provided below.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
