• Collaborate with the Technical Anchor to design, engineer, develop, deploy, and support new Azure AD B2C and EEID software and platform features that adhere to Ford security, architecture, and IAM standards. • Support delivery of tactical and strategic IAM solutions, including analysis, design, and implementation to improve security posture. • Contribute to the development and implementation of IAM strategies and roadmap. • Develop and maintain metric dashboards and monitoring/alerting systems. • Provide day-to-day operations of requests, incidents, and problems. • Create and maintain technical documentation for team and integrating applications (e.g., architecture diagrams, API documentation, coding standards, operational processes, procedures, configurations, application onboarding tips, etc.) • Use modern software development Agile methodologies to deliver high-quality software on time and on budget. • Use modern software delivery pipelines to automate the deployment and testing of software and platform features. • Maintain and update Ford custom policies using the Identity Experience Framework. • Maintain and update UI (visual presentation of user journeys) in accordance with the Ford UI Design Team provided specifications. • Write clean, efficient, and maintainable code in C#, .NET Core, or other relevant languages that can integrate with Azure Graph APIs

• Design and evolve the frontend platform foundations that power n8n’s workflow-building experience. • Enable product engineers and the open-source community to ship complex, reliable, and high-performance user experiences across cloud and self-hosted deployments. • Take ownership of core frontend platform domains and help shape the architecture behind one of the most flexible automation products in the market. • Own and evolve core frontend platform primitives that make n8n’s workflow builder scalable, extensible, and maintainable. • Strengthen platform contracts such as plugin systems, extension points, and frontend developer experience. • Help modularize a large, evolving frontend codebase so product teams can build confidently on top of shared foundations. • Work on the architecture behind n8n’s canvas-based workflow building experience. • Build and improve real-time collaboration capabilities using approaches such as WebSockets, CRDTs, or multiplayer synchronization patterns. • Design frontend systems with correctness, accessibility, and user experience in mind, especially for complex interactive workflows. • Improve rendering performance, runtime behavior, and responsiveness across complex workflow-building experiences. • Use performance measurement, profiling, and optimization techniques to identify bottlenecks and improve user experience. • Contribute to web worker orchestration and other frontend infrastructure that helps n8n scale to more complex use cases. • Drive technical decisions through clear proposals, RFCs, and collaborative engineering discussions. • Partner closely with product engineers, QA, DX, Security, Staff Architects, and the open-source community to ensure platform changes integrate smoothly. • Support peers through code reviews, pairing, mentoring, and knowledge sharing.

• Diseñar la arquitectura de la plataforma de datos de Humand. • Desarrollar e integrar información proveniente de sistemas internos, bases de datos, APIs y herramientas como HubSpot, Stripe, Amplitude y Redash. • Construir y evolucionar el Data Warehouse y los pipelines necesarios. • Modelar y automatizar métricas estratégicas, garantizando calidad, consistencia y trazabilidad de los datos. • Colaborar con los equipos de Producto, Ingeniería, BizOps y Finance para transformar necesidades de negocio en soluciones técnicas. • Evolucionar la plataforma para soportar capacidades de reporting, analítica e integración con herramientas externas de Business Intelligence.

Role Description We are seeking a ML Platform 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. Key Responsibilities - 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. 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. Preferred Qualifications - Open-source contributions to model serving infrastructure. - Experience with multi-region or globally distributed AI serving. - Familiarity with model quantization, distillation, and compression techniques. - Exposure to FinOps for AI workloads and cost-efficient serving design. - Experience supporting external-facing AI APIs at scale. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 505-3545. Learn more about Bright Vision Technologies at www.bvteck.com . We recognize that our people are our strength, and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company.