DevOps Engineer (AI)

Bayview Asset Management•Coral Gables, FL

2h•Remote

About The Position

Founded in 1993, Bayview Asset Management is an investment management firm focused on investments in mortgage and consumer credit, including whole loans, asset-backed securities, mortgage servicing rights, and other credit-related assets. Bayview Asset Management is seeking an Infrastructure Engineer to support the development and building and scaling of AI solutions across the firm. This role will sit within the AI Team and work closely with the IT team. However, they will own the infrastructure, data connectivity, and deployment pipelines required to build and operate AI products. The role is responsible for ensuring that data is accessible, systems are integrated, and solutions can be reliably deployed into production environments. This is not a traditional IT support role. It requires hands-on ownership of data pipelines, system integrations, and DevOps practices specific to AI and data-driven products.

Requirements

Strong experience with data pipelines, ETL processes, and system integrations
Strong, hands-on experience with CI/CD, containerization (Docker) and orchestration (Dagster, Airflow)
Deep operational experience with cloud platforms (AWS, Azure, or GCP)
Proven knowledge of leveraging IaC (Terraform, Azure Bicep) to rapidly deploy and evolve complex distributed system resources on the cloud
Ability to effectively improve and deploy observability across the stack
Deep experience triaging, resolving and communicating with relevant teams and stakeholders during as part of incident response
Ability to work across IT, engineering, and product teams
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Quantitative Finance, or a related field is required
5–8+ years in data engineering, DevOps, or infrastructure roles

Responsibilities

Build and manage infrastructure required for AI model development and deployment
Establish and maintain CI/CD pipelines for AI applications
Support model deployment, monitoring, and versioning
Ensure production systems are stable, scalable, and performant
Connect AI solutions into existing enterprise systems and workflows
Work closely with IT to align with enterprise architecture and standards
Ensure interoperability between AI tools, data platforms, and business applications
Implement DevOps best practices across AI projects
Monitor system performance, uptime, and reliability
Troubleshoot production issues and implement long-term fixes
Maintain logging, observability, and alerting systems
Design and build data pipelines to support AI use cases
Integrate existing data across internal systems, vendors, and external sources
Partner with data engineering, IT, and business teams to unlock critical datasets
Ensure alignment with enterprise IT and AI governance standards