The Principal Cloud Platform Architect will lead the design, implementation, and governance of enterprise-scale cloud foundations across AWS and Google Cloud Platform (GCP). This strategic role will help shape the organization's multi-cloud architecture, ensuring secure, scalable, and resilient infrastructure that supports global business operations and product development.
Lead the architecture, implementation, and governance of enterprise-wide cloud foundations across AWS and GCP, ensuring scalability, security, resilience, and operational consistency.
Design and manage cloud organization structures, including AWS Organizations, Organizational Units (OUs), Control Tower, landing zones, Service Control Policies (SCPs), and multi-account environments.
Establish and maintain enterprise cloud governance frameworks, architectural standards, security guardrails, and operating models that support multiple business units.
Define and oversee identity and access management (IAM) strategies, including federated identity, role-based access control, access governance, and enterprise cloud security.
Architect and optimize cloud networking environments, including Transit Gateway, Shared VPCs, hybrid connectivity, network segmentation, routing strategies, and DNS architecture.
Requirements
AWS Certified Solutions Architect – Professional certification
Design and implement agentic automation for business and engineering processes, build autonomous systems, and contribute to architecture and code quality.
Compensation
The salary for this position starts at EUR 55.000 per year (for 38.5 hours/week) and can be significantly higher depending on your qualifications.
Responsibilities
Design and implement agentic automation for business and engineering processes
Take ownership of architecture and implementation of complex, highly scalable multi-agent systems
Integrate LLMs with existing enterprise IT systems, engineering tools, and data pipelines
Apply clean code practices, design patterns, and test-driven development
Collaborate with domain experts, data engineers, and developers
Requirements
Completed degree in Computer Science, Software Engineering, Informatics, or a comparable field with a strong IT focus
Austrian HTL degree with several years of relevant professional experience
As a Forward Deployed Engineer, you help customers shape the future operating model using the latest AI, data, and cloud technologies, bridging consulting expertise with production-level software development.
Responsibilities
Translate unclear business problems and defined customer goals into clear technical implementation plans
Lead technical projects, coordinating sponsors, end-users, technical teams, and HSO colleagues
Develop functional, production-ready AI applications, agent-based workflows, automation solutions, and data-driven platforms
Design end-to-end solution architectures balancing speed, scalability, maintainability, security, and Responsible AI
Conceive, develop, integrate, test, and deploy enterprise solutions using modern cloud, data, and AI technologies
As a Platform Customer Engineer (CE), you will partner with technical Sales teams to differentiate Google Cloud to our customers. You will serve as the customer’s primary technical partner and trusted advisor, engaging in technical-led conversations to understand their business challenges. You will troubleshoot technical questions and roadblocks, engage in proofs-of-concepts and demos, and use your expertise to architect cross-pillar cloud solutions that solve these business challenges. You will drive the technical win and define the delivery and consumption plans.
Locations (2)
São Paulo, Brazil
Brasilia, Brazil
Responsibilities
Architect and manage Large Language Model (LLM) deployments across on-premises (NVIDIA/AMD) and cloud (cloud computing platform, Google Cloud platform (GCP) environments.
Audit multi-agent orchestration, agent construction, and vector databases to map data flows and enforce privilege boundaries.
Bridge local high-compute clusters and cloud AI services while maintaining a consistent security posture.
Use Docker and Kubernetes to orchestrate scalable inference and training environments, optimizing Graphics Processing Unit (GPU) utilization and resource isolation.
AI Developer
Babel (Grupo Babel / Babel Sistemas de Información) · Posted Nov 12, 2026
The Forward Deployed Engineer (Agentic AI) embeds with enterprise clients to design, build and operationalize agentic AI solutions in production, integrating agentic systems with enterprise platforms and systems of record.
Responsibilities
Lead technical discovery, scope agentic AI use cases, and translate business problems into engineering deliverables
Design target architectures that fit the client's environment, regulatory posture and platform preferences
Write production-grade code to deliver the solution
Build and tune retrieval pipelines, prompt architecture, guardrails, agent orchestration and human-in-the-loop controls
Implement enterprise-safe agent tool execution, including permission scoping, approval gates, audit trails and rollback paths
Requirements
Bachelor’s degree in computer science, engineering, information systems or related field, or equivalent practical experience
Design, develop, and deliver production-ready AI solutions for clients while maintaining hands‑on engineering involvement.
Compensation
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. A reasonable estimate of the current range is $102,400.00 - $204,100.00 per year.
Responsibilities
Architect end‑to‑end AI solutions including generative AI, agentic systems, and RAG
Lead solution architecture for AI build and proof‑of‑concept engagements
Remain hands‑on across solution development, coding, data integration, and debugging
Design and implement cloud‑native AI solutions on Azure, AWS, and GCP
Define architecture and integration patterns for LLMs, vector databases, APIs, microservices
Requirements
Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field
Nice to have: Master's degree in a technical field
Nice to have: cloud or architecture certifications
Nice to have: experience with OpenAI, Anthropic, Google LLMs
Nice to have: experience designing evaluation frameworks for LLMs
Lead client-facing AI engineering work within PwC’s Advisory practice, designing, implementing, and deploying AI solutions for health sector use cases.
Compensation
The salary range for this position is: $124,000 - $280,000. Actual compensation within the range will be dependent upon the individual's skills, experience, qualifications and location, and applicable employment laws. All hired individuals are eligible for an annual discretionary bonus.
Responsibilities
Leading applied AI and health system engineering workstreams from design through deployment for client engagements
Translating health services and clinical operations needs into AI solution requirements, reference architectures, and delivery roadmaps
Designing retrieval, memory, prompt, and agent orchestration components that support scalable model usage across production environments
Guiding data wrangling, model integration, and software implementation activities across cloud-based AI platforms
Evaluating model behavior through benchmark testing, human review, and scoring methods to validate performance and reliability
Requirements
Bachelor's degree
Nice to have: Experience delivering production AI or GenAI use cases in a health system environment
Nice to have: Hands‑on experience building RAG pipelines or agentic AI workflows against clinical data sources
Nice to have: Experience with MLOps platforms and model governance practices in regulated, PHI‑handling environments
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