Principal Software AI Engineer (R-20013)

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Key Responsibilities:: Define the architecture and technical direction for enterprise AI/ML and GenAI capabilities, including agentic and conversational AI solutions using platforms and models such as Vertex AI Agent Builder/ADK and Gemini Lead the design and delivery of production-grade LLM applications, including embeddings, RAG pipelines, vector database integration, tool use, orchestration, and multi-agent patterns Establish reusable AI engineering standards, reference architectures, design patterns, and governance controls for secure, responsible, observable, and maintainable AI solutions Provide technical leadership for end-to-end MLOps and CI/CD capabilities supporting model experimentation, evaluation, validation, deployment, monitoring, and lifecycle management Architect and guide scalable cloud-native AI services on GCP and Kubernetes using Docker, Infrastructure as Code such as Terraform, and event-driven integration patterns Lead the design of data pipelines, APIs, and enterprise integrations that embed AI/ML capabilities into business applications and collaboration platforms Define model and system evaluation approaches for quality, safety, latency, reliability, cost, and business effectiveness; guide production monitoring, incident response, root-cause analysis, and prevention Partner with Product Owners, Data Scientists, AI Data Engineers, Platform Architects, Security, Risk and Compliance, and application teams to evaluate trade-offs and deliver cost-effective solutions aligned to enterprise standards Lead technical design reviews, proofs of concept, and technology evaluations; influence build-versus-buy decisions and the adoption of emerging AI tools and platforms Provide training, guidance, and mentorship to engineers and raise engineering capability through code reviews, architecture reviews, knowledge sharing, and best-practice adoption Additional duties as assigned

Key Skills:: Bachelor’s degree in computer science, Engineering, or a related field; master’s degree preferred 12+ years of relevant software engineering experience, including substantial experience architecting and deploying production AI/ML solutions at enterprise scale Proven technical leadership in the architecture and delivery of large-scale, distributed AI systems across multiple teams or products Deep hands-on expertise with agentic AI frameworks and platforms, including Vertex AI Agent Builder / ADK, and large language models such as Gemini Expertise in Python application development and APIs, including frameworks such as FastAPI, with strong software design, testing, code review, and version-control practices Proven experience designing LLM-based applications, including RAG pipelines, embeddings, vector databases, tool integration, orchestration, prompt and context engineering, and model evaluation Deep experience with GenAI frameworks and ecosystems such as LangChain and Hugging Face Proven experience designing end-to-end MLOps capabilities using tools such as Vertex AI Pipelines, MLflow, or Kubeflow Advanced experience architecting cloud-native solutions on GCP; experience with AWS AI/ML services is a plus Proven experience deploying and operating scalable AI/ML services using Docker, Kubernetes, Terraform, CI/CD, observability, and incident-management practices Experience defining AI quality, safety, security, privacy, governance, performance, reliability, and cost-management standards for production systems Experience with e