Role Description We are seeking a Model Serving Engineer to design, build, and operate high-performance, highly reliable inference platforms for serving large machine learning models in production. The role focuses on the systems engineering side of AI deployment, including: - Request routing - Batching - Caching - Autoscaling - GPU utilization - End-to-end observability across diverse model workloads The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands the trade-offs between latency, throughput, cost, and quality in ML serving. Qualifications - Bachelor’s or Master’s degree in Computer Science or a related field. - Six or more years of experience in distributed systems, infrastructure, or ML platform engineering. - Strong proficiency in Python and a systems language such as Go, Rust, or C++. - Deep experience operating high-throughput, low-latency services in production. - Hands-on experience with LLM or large model inference frameworks such as vLLM or TensorRT-LLM. - Strong understanding of GPU architecture, memory hierarchies, and accelerator utilization. - Familiarity with Kubernetes, autoscaling, and modern cloud platforms. - Experience with observability stacks including metrics, tracing, and structured logging. - Solid grounding in performance engineering and capacity planning. - Strong communication and incident response skills. Requirements - Design and operate model serving platforms supporting diverse workloads including LLMs, vision models, and recommendation systems. - Optimize inference performance using continuous batching, paged attention, speculative decoding, and request multiplexing. - Implement multi-tenant routing, rate limiting, and quality-of-service policies across model endpoints. - Build autoscaling and capacity management systems that balance latency, throughput, and cost. - Tune GPU utilization, memory management, and KV cache strategies for LLM serving workloads. - Integrate model serving with API gateways, identity systems, and observability platforms. - Implement caching, prompt deduplication, and response reuse strategies where appropriate. - Drive end-to-end observability including latency histograms, queue dynamics, GPU utilization, and error tracking. - Develop deployment workflows including canary releases, shadow testing, and automated rollback. - Operate incident response for high-availability AI services and drive durable reliability improvements. - Collaborate with ML and product teams to support new model releases and capability rollouts. - Implement security controls including request signing, content filtering, and abuse detection at the serving layer. - Document operational procedures, performance characteristics, and tuning guidance for internal teams. - Stay current with AI serving research and translate advances into production capabilities. Benefits - Competitive base salary commensurate with experience, plus benefits.

Role Description We are looking for a Senior Director of Agentic AI Engineering to define, own, and execute the strategy for DigitalOcean’s next-generation agentic infrastructure services. This leader will be responsible for building and scaling products such as Open Harness Runtime, Action Toolbox, and other foundational services that make it simple for developers to run, scale, and operate agents in the cloud. This is a rare opportunity to shape a new product category at DigitalOcean while building with the operational discipline required to serve our most demanding AI customers at scale. What You’ll Do - Define and execute the technical roadmap: Own the multi-year engineering strategy and roadmap for DigitalOcean’s Agentic AI infrastructure services, ensuring customers have a seamless, high-performance environment for building and running agentic workloads. - Build next-generation agentic primitives: Lead the architecture and delivery of core agentic infrastructure capabilities, including Open Harness Runtime, Action Toolbox, Coding Execution Runtime, and new services that make it simple to run, scale, resume, monitor, and operate agents in the cloud. - Drive architectural excellence and operational scale: Set the long-term architecture for services that must be scalable, resilient, secure, and cost-efficient under large-scale agent deployment. Build the operational muscle required to scale usage, reliability, and customer trust. - Launch revenue-driving products: Partner with product and GTM teams to launch new SKUs and services purpose-built for agentic workloads, balancing performance, developer experience, and gross margin. - Improve efficiency and margin: Drive engineering initiatives that improve infrastructure utilization, reclaim margin, reduce waste, and prevent unnecessary CapEx growth as the business scales. - Lead strategic infrastructure transitions: Guide the planning and execution of new compute products and SKUs, including ARM and other specialized compute shapes, to improve power efficiency, density, cost structure, and workload fit. - Build a high-performing engineering organization: Lead, mentor, and scale a geographically distributed engineering team. Create a culture that balances rapid AI innovation with the operational rigor required for production infrastructure. Qualifications - Deep infrastructure expertise: Extensive experience building and operating cloud infrastructure, virtualization platforms, distributed compute systems, runtime environments, or developer infrastructure at scale. - Fluency in agentic AI infrastructure: A strong understanding of the infrastructure needs of agentic systems, including sandboxed execution, MicroVMs, long-running workloads, tool execution, state management, coding agents, and high-performance runtimes. - Strong business and technical judgment: Proven ability to translate business goals such as revenue growth, margin improvement, utilization, and efficiency into technical strategy, architecture, and execution. - Operational excellence at scale: Experience operating production systems with high reliability requirements, clear ownership models, measurable SLOs, incident discipline, and continuous improvement loops. - Executive leadership: Experience leading large, distributed engineering teams through technical and organizational change, with a track record of developing leaders, setting clear accountability, and delivering through complexity. - Customer obsession: A demonstrated ability to work backwards from demanding customers, turn ambiguity into product direction, and build infrastructure that developers love because it is powerful, reliable, and simple to use. Requirements - Compensation Range: $261,600 - $327,000 - This is a remote role - JR: 2026-8058 - #LI-Remote Benefits - We innovate with purpose: Be part of a cutting-edge technology company with an upward trajectory, simplifying cloud and AI. - We prioritize career development: Access to resources for growth, reimbursement for relevant conferences, training, and education, and LinkedIn Learning's 10,000+ courses. - We care about your well-being: Competitive benefits including Employee Assistance Program, Local Employee Meetups, and flexible time off policy. - We reward our employees: Salary based on market data, potential bonuses, and equity compensation for eligible employees. Company Description DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service. Application Limit: You may apply to a maximum of 3 positions within any 180-day period.

Role Description Legrand North America has a remote opportunity for a Senior SAP Commerce Platform Engineer to join our Enterprise Digital Systems team. This is a deep platform engineering role (not a maintenance role). - Build and extend the SAP Commerce backend. - Integrate it with upstream enterprise systems through middleware. - Own problems end to end — from the storefront request down through the service and persistence layers to the integration boundary. - The platform serves multiple brands and storefronts and is evolving toward a composable, headless architecture. - Operate independently inside the platform: reason through the full stack, diagnose production issues by working directly through SAP Commerce and Dynatrace logs, and resolve them at the root. At a Glance: - Work inside a centralized SAP Commerce backend that powers multiple brand storefronts. - Hands-on backend engineering: building and maintaining custom SAP Commerce extensions — type system, service layer, interceptors, cron jobs, ImpEx, integration logic. - Independent troubleshooting through SAP Commerce Cloud and Dynatrace is a daily expectation. - The platform is moving toward headless / composable (OCC APIs, headless content, search). - This role reports into Platform Engineering and partners with the platform architect on design and estimate validation. Qualifications - Experience with SAP Commerce backend development. - Strong understanding of middleware integration with enterprise systems (ERP, PIM/Stibo, CRM). - Ability to troubleshoot production issues independently. - Experience with headless architecture and APIs. Requirements - Proven experience in backend engineering. - Ability to operate with independent technical direction. - Strong problem-solving skills and attention to detail. Benefits - Remote work opportunity. - Collaborative team environment. - Opportunities for professional growth and development.

• Define the platform vision and develop a comprehensive understanding of the architecture, capabilities, and limitations across all owning and consuming teams. • Partner with platform engineers and technical leads to define requirements, constraints, and success criteria for platform investments incorporating infrastructure capabilities and customer needs into clear, actionable specifications. • Partner closely with backend engineering, infrastructure teams, and data science to drive efficiency, improve consistency, simplify processing, and align document management functionality with the broader platform. • Own the adoption and migration strategy for platform functionality, working through influence, demonstrated value, and consensus-building to drive teams toward shared solutions. • Monitor, analyze, and optimize performance, latency, cost-to-serve, and core engineering metrics. In collaboration with machine learning engineers, track AI platform outcomes, including data extraction accuracy, ingestion throughput, indexing latency, and search precision/recall. • Proactively build awareness of document platform capabilities across the organization. Mentor Associate and mid-level Product Managers in technical product processes, data-driven decision making, and infrastructure leadership.