AI DevOps Engineer Jobs

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Participate in on-call rotation to respond to production incidents, build and run infrastructure with Ansible, Terraform, and Kubernetes to enable scaling to a massive number of concurrent users, build monitoring systems to ensure the highest quality service for customers, design and implement operational processes such as deployments and upgrades, debug production issues across all services and levels of the stack, identify improvements for product architecture from reliability, performance, and availability perspectives, and plan the growth of Together AI's infrastructure.

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In this role, you will spin up and scale large Kubernetes clusters, including automating provisioning, bootstrapping, and cluster lifecycle management; build software abstractions that unify multiple clusters and provide a seamless interface to training workloads; own node bring-up from bare metal through firmware upgrades ensuring fast and repeatable deployment at massive scale; improve operational metrics such as reducing cluster restart times and accelerating firmware or OS upgrade cycles; integrate networking and hardware health systems to deliver end-to-end reliability across servers, switches, and data center infrastructure; develop monitoring and observability systems to detect issues early and maintain cluster stability under extreme load; solve real-time operational challenges, diagnose and fix issues quickly, and continuously improve automation, resilience, performance, and uptime across the systems powering frontier AI model training.

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Build and deploy AI agents including prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. Act as the primary technical partner for customers by leading demos, communicating progress, gathering feedback, and guiding solutions from concept to production. Configure and connect systems via APIs, handling authentication, data mapping, error handling, and integrations with CRMs, knowledge bases, and other enterprise tools. Set up telephony integration including SIP/CCaaS/PSTN routing, metadata passing, fallback configurations, and troubleshooting call quality. Write and refine prompts for LLM-driven agents, monitor performance, conduct iterative testing, and ensure agents meet automation and containment targets. Translate customer requirements into actionable solutions and work consultatively to resolve challenges related to security, connectivity, or knowledge ingestion. Collaborate with product and engineering teams to address platform gaps, resolve technical issues, and lead client implementations independently.

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Debug and fix issues in the platform and ship pull requests with your fixes. Build internal tools and copilots powered by generative AI to enhance the team. Rapidly prototype proof-of-concepts for customer use cases. Work across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.

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Debug and fix issues in the platform and ship pull requests with fixes. Build internal tools and copilots powered by generative AI to enhance the team. Rapidly prototype proof-of-concepts for customer use cases. Collaborate across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.

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Debug and fix issues in the platform and ship pull requests with fixes. Build internal tools and copilots powered by generative AI to enhance the team. Rapidly prototype proof-of-concept solutions for customer use cases. Collaborate across Engineering, Product, and Solutions teams to unblock customers and push the boundaries of AI adoption.

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Translate business requirements into requirements for AI/ML models; prepare data to train and evaluate AI/ML/DL models; build AI/ML/DL models by applying state-of-the-art algorithms, especially transformers; leverage existing algorithms from academic or industrial research when applicable; test, evaluate, and benchmark AI/ML/DL models and publish the models, data sets, and evaluations; deploy models in production by containerizing them; work with customers and internal employees to refine model quality; establish continuous learning pipelines for models using online or transfer learning; build and deploy containerized applications on cloud or on-premise environments.

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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.

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Lead the design, building, deployment, and optimization of enterprise-grade AI agents including voice, chat, and AI copilots. Own the full lifecycle of AI agent development including prompt engineering, workflow creation, API integration, telephony setup, and evaluation forms. Engage with clients through weekly demos, progress updates, feedback gathering, and act as the primary technical contact for deployed solutions. Configure system integrations involving APIs, data maps, authentication, and connectivity to CRM, databases, and knowledge systems. Set up telephony routing (SIP/CCaaS/PSTN), manage metadata, configure fallbacks, and troubleshoot call quality issues. Monitor agent performance and iteratively refine prompts to meet automation and containment goals. Work strategically to translate customer requirements into technical solutions, addressing challenges related to security, connectivity, and knowledge ingestion. Collaborate with product and engineering teams to support deep technical fixes and platform development while independently leading client delivery and support.

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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.