Senior AI/ML Infra & SRE 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 – AI/ML Stack Infrastructure 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. Infrastructure Engineer – AI/ML Workflows 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. Staff DevOps Engineer – AI Workloads The Staff DevOps 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 GitOps 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 security best practices such as least-privilege access, encryption at rest and in transit, network segmentation, and automated compliance validation. The engineer will lead incident response and reliability initiatives, participate in on-call rotation, conduct post-mortems, and drive continuous improvement for system reliability. Architecting disaster recovery and business continuity strategies with automated backup, failover, and recovery processes is required. They will develop reusable infrastructure modules and templates to accelerate environment provisioning and standardize deployment patterns. Mentoring mid-level and senior engineers on cloud architecture, DevOps best practices, and platform reliability through design reviews and technical guidance is part of the role. They will also drive technical documentation and knowledge sharing including runbooks, architecture decision records, and infrastructure standards. Site Reliability Engineer, Inference Infrastructure As a Site Reliability Engineer on the Model Serving team, you will build self-service systems that automate managing, deploying, and operating services, including custom Kubernetes operators supporting language model deployments. You will automate environment observability and resilience, enabling all developers to troubleshoot and resolve problems, and take steps to ensure defined SLOs are met, including participating in an on-call rotation. Additionally, you will build strong relationships with internal developers and influence the Infrastructure team’s roadmap based on their feedback, as well as develop the team through knowledge sharing and an active review process. Location San Francisco or New York, United States #J-18808-Ljbffr AI Chopping Block, Inc.