Principal Engineer

Salary Range: $215,000.00 To $265,000.00 Annually Principal Engineer Role Title: Principal Engineer Descriptor: Agentic Operations & Cloud Modernization Location: Remote (US-based) Reports To: Head of AI Transformation Employment Type: Full-Time 1. Role Overview We are seeking an experienced, hands-on Principal Engineer reporting directly to the Head of AI Transformation to serve as technical architect and execution driver across our enterprise agentic AI ecosystem, cloud transformation, and modernization initiatives. You will work with leadership to shape the technical direction, then own its execution: architecting systems for autonomous AI agents, orchestrating complex workflows, building cloud-native systems on GCP, and driving modern CMS/web platform architecture. You will partner directly with our strategic implementation and cloud transformation partners, steering joint workstreams, ensuring code and architectural rigor, and managing operational knowledge transfer. You will also guide the bridge between existing systems and target architecture: evaluating legacy services (primarily .NET/C# monoliths and scheduled workflows), decomposing and migrating them to modern GCP services. 2. Key Responsibilities Strategic Partner Collaboration & AI Workload Delivery Partner Co-Engineering & Technical Direction: Serve as the primary internal technical counterpart to our strategic technology and implementation partners across active AI transformation workstreams. Joint Workload Delivery: Co-architect, review, and validate milestones with external engineering teams, ensuring AI models, agentic workflows, and cloud deployments align with architectural guidelines and quality benchmarks. Knowledge Transfer & Hand-off: Establish architectural review mechanisms, joint CI/CD standards, and documentation so internal teams can take operational ownership of partner-developed systems. Agentic Systems Architecture & Engineering Multi-Agent Orchestration: Design, implement, and deploy production-grade multi-agent architectures with dynamic tool-calling, autonomous reasoning loops, persistent memory, and deterministic human-in-the-loop workflows. Evaluation & Guardrails: Establish rigor around agent performance, reliability, and security through automated evaluation suites, hallucination guards, latency controls, and safety filters. Platform Integration: Connect agentic layers to internal data stores, enterprise APIs, vector databases, and external enterprise SaaS platforms. Google Cloud Infrastructure & Modernization Cloud Architecture: Architect scalable, secure, resilient cloud foundations on GCP (Vertex AI, Cloud Run, GKE, Pub/Sub, BigQuery, Cloud Functions). Infrastructure as Code & CI/CD: Establish enterprise DevOps and GitOps practices using Terraform, containerized microservices, automated testing, and secure secret management. Governance & FinOps: Implement IAM least-privilege models, cost monitoring, and resource optimization across cloud subscriptions and AI API usage. Modern CMS & Digital Platform Architecture Modern Web Platform Architecture: Architect and drive the transition from legacy monolithic website backends to API-first headless CMS and composable web architectures. Lead Generation & Data Flow: Connect modern CMS frontends with backend CRM, marketing automation, and AI-driven personalization workflows. Performance & Scalability: Optimize web delivery performance, CDN caching, edge routing, and SEO-friendly architectures. Legacy Bridge & .NET Migration Strategy Workload Deconstruction: Analyze existing backend systems and workflows (predominantly .NET/C#) and develop phased migration and decoupling strategies. Data & Event Pipelines: Implement event bridges and modern API gateways so legacy systems and new cloud-native agentic pipelines operate together during transition. Shared Platform Core & Cross-Coverage Shared platform core: GCP, Terraform, CI/CD, IAM, Cloud Run, and observability are shared responsibilities between this role and the Senior Cloud Platform Engineer. Primary backup to the Senior Cloud Platform Engineer for Terraform, pipelines, and enterprise integrations. Documentation: Document architecture and operational procedures so no production system depends on one person. 3. Candidate Requirements Required Qualifications & Experience 10+ years of progressive experience in software engineering, backend architecture, and distributed systems. 3+ years in a Staff or Principal Engineer capacity, acting as technical architect and driving execution on complex cross-functional systems. Agentic AI delivery: Production multi-agent systems built with frameworks such as LangGraph, CrewAI, AutoGen, Vertex AI Agent Builder, or custom orchestration engines. Google Cloud Platform: Hands-on architecture across Vertex AI, Cloud Run, Pub/Sub, Cloud Storage, BigQuery, VPCs, and IAM. Partner and vendor co-delivery: Track record collaborating with external development squads, systems integrators, or cloud partners to co-develop, review, and operationalize mission-critical workloads. Distributed systems: Asynchronous messaging, event-driven architectures, RESTful APIs, containerization, and CI/CD pipelines. Bachelor's or Master's degree in Computer Science, Software Engineering, or equivalent practical experience. Preferred Qualifications Modern CMS and web platforms: Architecting or replatforming on headless/composable CMS, API-first architectures, and modern web frameworks. Observability and LLM-Ops: Telemetry, tracing, and monitoring for LLM and agentic workloads (e.g., LangSmith, Arize Phoenix, GCP Cloud Trace/Monitoring). Kubernetes (GKE) and gRPC depth. Legacy .NET / C#: Experience with .NET Core and legacy .NET Framework, enabling you to inspect legacy codebases, decouple monoliths, and design clean migration paths. Enterprise cloud migrations: Leading migrations from on-premises or Microsoft-centric environments (Azure / Windows Server) to Google Cloud with zero operational downtime. Data integration and vector search: Vector indexing, semantic search, and enterprise data warehousing.