AI DevOps Engineer

Senior Infrastructure Engineer – Bland

As a Senior Infrastructure Engineer at Bland, responsibilities include contributing to the design of scalable architecture by building distributed systems using Kubernetes that handle high-volume, real-time voice processing with strict latency and reliability requirements; building and supporting machine learning infrastructure including training pipelines and real-time inference serving across multiple regions; maintaining robust integrations with enterprise telephony systems, SIP trunks, and VoIP infrastructure;

identifying architectural flaws and solving them; ensuring platform reliability through monitoring, alerting, and incident response systems to maintain enterprise-grade uptime; anticipating and solving scaling challenges related to exponential call volume growth; and implementing security best practices and compliance requirements for enterprise customers in regulated industries.

Lead the team responsible for the infrastructure supporting AI/ML Stack, focusing on scalability and efficiency of the Machine Learning Operations platform. Develop and execute the long-term vision and roadmap for the MLOps team to support ML development and deployment across business units, balancing short-term tactical deliveries with long-term architectural transformation. Manage and mentor a team of 6-7+ engineers, allocating resources strategically to support existing services and execute key strategic initiatives.

Collaborate cross-functionally with leaders in machine learning, data science, product engineering, and infrastructure to identify pain points, remove bottlenecks, and facilitate new solution deployment. Architect compute and storage pipelines for ML Engineers to manage large datasets and artifacts efficiently. Modernize the AI product inference stack for significant growth in global deployments. Work with Site Reliability Engineering to establish comprehensive system observability metrics.

Conduct assessments for technology refresh and benchmark proprietary tools against commercial and open-source alternatives to meet future needs.

The Infrastructure Engineer is responsible for building robust, secure, and scalable cloud infrastructure to support AI and machine learning workflows. This includes designing, building, and deploying cloud infrastructure, partnering with technical and non-technical stakeholders from idea generation through implementation and shipping, enabling Machine Learning Engineers and Data Scientists by contributing to internal best practices, standards, and reusable code repositories, proactively identifying and recommending ways customers can leverage cloud infrastructure to solve key challenges, creating and maintaining reusable, company-wide libraries and infrastructure-as-code, and researching and integrating the best open-source technologies to enhance Faculty's infrastructure capabilities.

The Staff Dev Ops Engineer will design and architect secure, scalable cloud and edge infrastructure for deploying AI workloads across multi-cloud and hybrid environments. They will build and maintain production-grade Infrastructure as Code using tools like Terraform, Ansible, or Pulumi, managing over 100 resources with Git Ops workflows and automated validation. The role includes designing and operating production Kubernetes clusters optimized for AI/ML workloads with GPU support, implementing container security, multi-tenancy, and resource optimization.

They will implement secure CI/CD pipelines with integrated security controls and automated deployment workflows for containerized AI models. The engineer will lead MLOps infrastructure initiatives including model deployment pipelines, versioning, feature stores, experiment tracking, and monitoring for model performance and drift. Responsibilities also include designing comprehensive observability and monitoring solutions using tools like Prometheus, Grafana, ELK, or Datadog with distributed tracing, application performance monitoring, and real-time alerting.

They will implement…