Senior Machine Learning Engineer

Location: Hybrid Work Model - Dublin. OH

The Data Science Center of Excellence Team at Central is looking for a passionate machine learning engineer to work on AI/ML solutions in support of our business stakeholders at Central.

Our team provides analytical solutions that support claims, services, and marketing. As a machine learning engineer, you will be responsible for deploying models in the cloud as well as on-premises, shared Python frameworks, and data pipelines that integrate AI/ML models with Central’s system and customer-facing applications.

As a Sr. Machine Learning Engineer focused on AI/ML model training and inference pipelines, you will use your technical skills, as well as your domain expertise, to develop innovative solutions. If you have a desire to deliver business value through technology designs in an environment that will allow you to grow and innovate, we will be delighted to have you.

How You Will Make an Impact

• Understand business objectives, product or business requirements and develop solution that achieve them
• Present complex analyses clearly and concisely
• Ability to build collaborative relationships with peers and multi-functional partners
• Provide live on-call support by participating in the team on-call rotation and owning production issues from root cause analysis to resolution to future prevention
• Develop ML workflows and end-to-end pipelines for data preparation, training, deployment, monitoring and ensure that the quality of architecture and designs of ML systems
• Drive code reviews to ensure quality, maintainability, and adherence to coding standards
• Collaborate closely with product teams to lead and own API development, maintaining ML infrastructure, and seamlessly integrating machine learning features into products
• Conducts reasonably complex statistical analysis, including predictive and prescriptive modeling
• Creates solutions for business problems that can be integrated into internal systems or software
• Ensure data quality, security and availability for the data, notebooks, models, experiments, and applications.
• Consult with data engineers in the development of data pipelines, packages, and tools that the data science team utilizes as a key stakeholder
• Technical and programmatic support through the entire project life cycle including concept development, system definition, acquisition planning, source selection, system design, development, integration and test, system delivery, and deployment
• Build production-grade solutions to scale, manage, and serve machine learning models and data science solutions
• Write and edit documentation and technical requirements, including evaluation plans, confluence pages, white papers, presentations, test results, technical manual, formal recommendations and reports
• Provide guidance and mentorship to junior engineers, fostering growth and development within the team

What You Will Do

• Master’s degree in a technology related field such as computer science, engineering, applied statistics and 2 years experience in machine learning
  • Or Bachelors degree and 4 years experience in machine learning
  • Or 6 years experience in machine learning
• Proven experience with continuous integration & delivery (CI/CD) practices and tools (Git, Jenkins, uDeploy).
• Proven experience with building container-based systems such as Docker.
• Proven experience with relational and dimensional data modeling techniques.
• Proven experience or strong interest in machine learning models and concepts: regression, random forest, boosting, NLP, and deep learning.
• Proven experience or strong interest in developing and deploying complex and scalable software systems in the analytics space.
• Experience with Azure Data Lake or other cloud data warehousing solutions.
• Exposure with orchestration and scheduling tools (Control-M)
• Proven experience with triaging, troubleshooting, and fixing issues in Production environments
• Excellent communication skills including written, verbal, and technology diagrams.
• Understanding of the model development lifecycle and has had exposure to DevOps/MLOps/LLMOps/ModelOps
• Capable of working independently
• Experience with enterprise Cloud ML Services (i.e., Sagemaker, AzureML, Vertex AI), and open source AI/ML frameworks
• Ability to understand Central Insurance’s policies and processes

Preferred

• Azure certification or AWS certification