Senior Platform Engineer responsible for designing and improving the AWS serverless data platform at IMS, a leading connected insurance and telematics provider.
About the role
- Cloud & AI Platform Engineer responsible for designing AI infrastructure and collaborating with teams at Manulife. Requires 10+ years of experience and a Bachelor's degree in a related field.
Responsibilities
- Design scalable and robust AI infrastructure that supports model training, deployment, and management
- Update and maintain AI models and infrastructure as technology and requirements evolve
- Stay updated with the latest advancements in AI and machine learning technologies
- Experiment with new tools and techniques to improve cloud data lake platform and models
- Gain deep understanding of data by collaborating with business and advanced analytics partners; demonstrate the understanding to design, curate and publish connected data sets that enables users to self-serve
- Lead on ensuring our Azure applications in prod run within SLA and ensure their robustness and security.
- Propose technology improvements or come up with innovative solutions to solve business problems.
- Participate and propose best solution for AI projects
- Collaborate with Architecture, security, and risk teams and implement latest guidelines and Azure standard methodologies
- Lead, mentor and guide data engineers to promote customer first approach
- Automate infrastructure provisioning and deployment using tools such as terraform
- Implement CI/CD pipelines that enables automated code deployment
- Participate in Agile sprints and ceremonies; supports rapid iteration and development
- Take lead in maintaining the inventory of critical data elements and lineage
- Cultivate and maintain strong relationships and fosters collaboration with various teams and partners within the organization
- Provide guidance and mentorship to junior engineers and other team members.
Requirements
- Bachelor’s in Computer / IT or data related fields required
- Minimum 10+ years IT industry experience is required
- 7 to 10 years of previous platform engineering experience, with experience working in enabling azure related data technologies
- 1-2 years of experience in model deployments and infrastructure deployments for AI
- Solid understanding of Azure infrastructure; subscriptions, resource groups, resources, access control with RBAC (role-based access control), integrations with Azure AD and Azure security principles (user group, service principal, managed identity), network concepts (VNet, Subnet, NSG rules, private endpoints), password\credential\key management and data protection
- Strong hands-on knowledge of Azure Databricks, ADF, ADLS, Synapse Serverless/dedicated/spark pools, Python, PySpark, and T-SQL along with experience designing and developing scripts for ETL processes and automation in Azure Data Factory and Azure Databricks
- High proficiency in GIT/Jenkins/dev ops processes to maintain and resolve issues with data pipelines in production
- Good understanding of data modeling, data mart, data Lakehouse architecture, SCD, data mesh and delta lake overall
- Solid understanding of data privacy and compliance regulations and standard methodologies for preserving customer data
- Experience deploying LLM models & monitoring AI infrastructure in cloud environments
- Knowledge of implementing azure technologies and networking via terraform along with ability to fix issues with Azure infrastructure in production.
Benefits
- health, dental, mental health, vision, short- and long-term disability, life and AD&D insurance coverage
- adoption/surrogacy and wellness benefits
- employee/family assistance plans
- retirement savings plans (including pension and a global share ownership plan with employer matching contributions)
- financial education and counseling resources
- generous paid time off program
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