• Our Principal Cloud Engineer is a senior technical leader responsible for designing, governing, and optimizing cloud infrastructure and platforms across the organization. • In this role, you will serve as the organization's senior technical authority for cloud architecture and infrastructure, bringing extensive expertise to establish engineering standards and governance, and to lead complex cloud initiatives from design through delivery. • Beyond technical leadership, you will mentor and guide the Systems Administration and Information Security teams, ensuring knowledge is shared through documentation, standards, and coaching. • You will translate complex technical concepts into clear business and operational decisions, provide leadership during critical production events, and set the benchmark for engineering excellence. • Roles: Serve as the highest-level technical authority and subject matter expert for all Azure cloud infrastructure and architecture • Define cloud engineering standards, architectural patterns, and governance practices that the organization adopts and follows • Own and lead cloud infrastructure projects end-to-end — from design and planning through execution, delivery, and post-deployment operations • Support the Manager of IT & IS on cloud strategy, architecture roadmap, and platform investment decisions • Mentor and develop the Systems Administration and Information Security teams — building organizational cloud competency through hands-on guidance, deliberate coaching, and knowledge transfer • Collaborate with Development teams to design and deliver application infrastructure on Azure • Provide Tier 2/3 escalation support to the IT Help Desk for complex infrastructure and cloud issues • Partner with the Information Security team to implement and evidence compliance controls for HITRUST 11.3r2 and SOC2

• Embed within customer engineering teams and lead technical discovery sessions with business stakeholders, engineering leadership, and security to translate ambiguous business problems into clear AI architectures and delivery plans. • Architect, code, and ship production-grade agentic AI solutions on Google Cloud — including multi-agent systems, MCP servers, sub-agents, skills, connectors, agentic wrappers, and safety guardrails — that move customers beyond pilots into measurable business value. • Design and implement Retrieval-Augmented Generation (RAG) pipelines and grounding architectures, including chunking strategy, vector databases, and embedding optimization to prevent hallucinations and ensure response quality. • Build the “connective tissue” between Google’s AI products and customer infrastructure, including APIs, legacy data silos, identity, and security perimeters. • Implement multi-agent patterns such as ReAct, self-reflection, and hierarchical delegation using frameworks like Google’s Agent Development Kit (ADK) or LangGraph • Build high-performance evaluation pipelines and observability frameworks for agentic systems, with attention to accuracy, safety, latency, cost-per-request, and tokens-per-second. • Debug agent logic and optimize tool selection in live, high-traffic environments, including tracing conversation and request IDs across microservices to resolve production failures. • Co-build with customer engineering teams and act as a hands-on advocate for AI-assisted development, introducing and operationalizing AI coding tools to accelerate delivery and elevate engineering practices. • Drive a deliberate handoff to the customer’s team, ensuring long-term ownership, documentation, and end-user adoption after the engagement concludes. • Develop and maintain technical documentation, architecture decision records, and evaluation results across all assigned engagements.

• Advising our clients and working on platforms such as Google Cloud Platform as well as AWS, Azure, Alibaba Cloud or hybrid environments • Point of contact and implementer for technical inquiries and solutions (in the context of Technical FinOps, Cloud Governance, architecture, Kubernetes, migration strategies, etc.) • Designing and developing migration strategies in cloud computing with consideration for FinOps • Technical consulting for the implementation of cloud solutions (AWS, Azure, GCP, etc.) across different platforms and their technical implementation • Advising on the adoption of FinOps principles and FinOps strategies • Standardizing Continuous Integration and Continuous Delivery • Evaluating system and software engineering tools and processes • Developing system specifications for our clients' environments

• Work across the full development cycle (design → build → test → deploy → operate) with AI support • Use agents and automations to execute multi-step tasks • Structure inputs (prompts, specs) to guide and control AI • Integrate with engineering tools: Git, CI/CD, observability • Evaluate the quality and safety of AI-generated outputs • Understand the architecture behind generated solutions, not just operate the tools