Role Description Allocate is looking to add a Platform Engineer to the team! There's a lot for us to build, and we need a versatile engineer with a broad skillset who can jump right in to help us lay the technical foundation for the company's future. - Design, build, and operate reliable, scalable backend services and data pipelines. - Work across our cloud infrastructure (AWS) to ship and run production systems. - Improve the performance, reliability, and observability of the systems you own. - Automate manual processes and reduce operational overhead. - Write high-quality, well-tested, maintainable code as part of a cross-functional squad. Qualifications - 4+ years of backend or data engineering experience, with strong Python (or a similar language). - Hands-on experience building and operating production systems on AWS. - Experience designing reliable, scalable data pipelines or backend services. - Solid SQL and data fundamentals. - Proficiency with Git and version control systems in general. - Hands-on experience with AI-assisted development tools such as Claude Code, OpenAI's Codex, or Cursor, and a demonstrated ability to continuously evaluate and incorporate new tools as they emerge. Requirements - Docker and containerized deployments (e.g., AWS ECS/EKS). - Snowflake or other modern data warehouses. - Workflow or process automation experience. - Background in fintech or financial data. Education - Bachelor's degree in Computer Science, a similar technical field, or equivalent practical experience. Benefits - Medical, dental, and vision; 401(k); and responsible vacation time (PTO). - Travel required for team and department offsites. - An in-person interview may be required during the process. - A broadband internet connection is required. - Compliance with Allocate's Code of Ethics is a given for this role.

Role Description Design and lead the evolution of enterprise-wide AI platforms and architecture to enable AI-powered products, intelligent workflows, and scalable digital transformation. This role combines enterprise architecture, platform engineering, and AI delivery to build solutions that are secure, scalable, and operationally viable. The AI Platform Architect ensures end-to-end alignment from strategy through implementation, balancing innovation, governance, and enterprise usability. - Define and drive enterprise architecture strategy across AI, data, cloud, and platform ecosystems - Architect and evolve enterprise AI platforms, including LLM pipelines, RAG systems, and agentic workflows - Design intelligent applications, copilots, and conversational AI solutions embedded into business processes - Lead end-to-end solution architecture, from concept and prototyping through implementation and scale - Develop target architectures, reusable patterns, and capability roadmaps for AI adoption - Design and manage hybrid, multi-cloud, and on-prem infrastructure (AWS, Azure, GCP, GPU environments) - Define and implement API strategy, integration patterns, and enterprise governance frameworks - Establish DevSecOps practices, including CI/CD, Infrastructure as Code, and automated deployment pipelines - Architect data platforms, knowledge systems, and analytics capabilities to enable AI and decision-making - Enable enterprise integration across ERP, CRM, data platforms, and workflow systems - Ensure security, compliance, and responsible AI practices aligned with regulatory requirements - Lead hands-on prototyping and solution validation to accelerate delivery and reduce risk - Provide senior stakeholder and board-level advisory on architecture decisions, risks, and trade-offs - Drive vendor evaluation, technology selection, and AI tooling strategy - Support and mentor engineering teams to ensure effective execution of architecture designs Qualifications - Strong experience in enterprise and solution architecture across complex environments - Ability to define architecture principles, governance models, and long-term technology strategy - Expertise in platform thinking and enterprise system design - Experience with AI/ML platforms, including LLMs, RAG, semantic search, and agent-based systems - Understanding of AI-enabled workflows, knowledge systems, and enterprise AI adoption - Ability to design scalable, production-grade AI platforms - Strong foundation in cloud (AWS, Azure, GCP), hybrid, and on-prem architectures - Experience with GPU infrastructure, model deployment, and inference optimization - Proficiency in APIs (REST, GraphQL), microservices, and distributed systems - Hands-on experience with DevSecOps, CI/CD pipelines, and Infrastructure as Code (Terraform) - Knowledge of data platforms, ETL, data warehousing, and analytics systems - Expertise in enterprise integration patterns and API governance - Strong understanding of security, identity (SSO, OAuth2/OIDC), and compliance frameworks (ISO27001, GDPR, etc.) - Ability to influence senior stakeholders and cross-functional teams - Strong communication translating technical architecture into business outcomes - Experience leading engineering and architecture teams - Balanced approach to innovation, governance, cost, and scalability Requirements - 12+ years of experience in software engineering, AI/ML systems, or platform architecture, with strong focus on Generative AI - Proven experience acting as Senior Principal / Enterprise Technical Architect within large Engineering organizations - Demonstrated ability to define architecture standards and guide large engineering teams - Strong stakeholder engagement skills across engineering leadership and executive teams - Self-driven, collaborative, and comfortable working in fast-paced engineering environments - Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or related field - Cloud or AI certifications (Azure Developer, Azure AI Engineer, or equivalent) preferred Benefits - Equal opportunity employer that considers all qualified applicants without regard to age, blood type, caste, citizenship, color, disability, family medical history, family status, ethnicity, gender, gender expression, gender identity, genetic information, marital status, national origin, parental status, pregnancy, race, religion, sex, sexual orientation, transgender status, veteran status or any other category protected by law. - Local employment practices and rights may vary by jurisdiction and are subject to applicable local laws. - Commitment to provide for all those who seek to express interest in employment the opportunity to participate without barriers.

• Construir e evoluir plataformas e serviços internos que abstraem a complexidade de infraestrutura para os times de produto • Escrever software de verdade (não apenas operar ferramentas): automações, integrações e blocos reutilizáveis de plataforma • Trabalhar com infraestrutura como código, containers, Kubernetes e fluxos GitOps • Criar capacidades de self-service, caminhos prontos e a documentação que faz tudo isso ser adotado • Colaborar de perto com o time de Segurança e com as equipes de produto para que a plataforma nasça confiável e adotável • Tomar decisões de arquitetura e sustentar o trade-off técnico junto à liderança

• As a Staff Software Engineer, you will play a critical leadership role in building and operating the infrastructure that powers Cake’s AI platform. • You’ll design and operate the ML platform foundations that both internal teams and customers rely on, owning systems end-to-end from architecture to production. • You’ll work closely with customers to translate real-world ML use cases into reliable, scalable platform capabilities. • Leverage infrastructure-as-code to manage complex cloud environments supporting critical ML and AI initiatives. • Design Kubernetes-native systems, including controllers/operators where appropriate. • Improve platform networking, security, and observability. • Own critical systems in production, including reliability, scalability, security, and cost efficiency. • Identify and proactively address technical debt, operational risk, and platform bottlenecks. • Quickly ramp up to a complex tech stack (Terraform, Kubernetes, Istio, Crossplane, Go, TypeScript). • Create abstractions and tooling that make it easier for teams and customers to deploy, run, and scale AI/ML workloads. • Collaborate directly with customers to understand their ML infrastructure challenges and translate them into platform improvements. • Balance speed and rigor—shipping quickly while maintaining a high bar for quality and safety. • Act as a technical leader and mentor across the engineering organization. • Write clear documentation and design proposals that align stakeholders and drive decisions. • Partner closely with product and leadership to shape platform direction and priorities.