Principal Software AI Engineer (R-20113)

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Job Summary:
 
The Principal AI Software Engineer is a principal-level individual contributor responsible for defining technical direction and leading the architecture, design, and delivery of enterprise-scale AI/ML and Generative AI solutions. The role provides deep expertise in large language models (LLMs), agentic AI systems, Retrieval-Augmented Generation (RAG), model evaluation, and end-to-end MLOps, with a focus on secure, reliable, cloud-native services on Google Cloud Platform (GCP). The Principal AI Software Engineer partners across product, data science, engineering, architecture, security, risk, and cloud operations to translate strategic business needs into scalable AI capabilities, establish engineering standards and reusable patterns, and provide technical leadership and mentorship across teams.

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 event-driven workflows, enterprise data pipelines, and integrations such as Microsoft Teams and Microsoft Graph API Experience with multimodal AI solutions spanning text, image, and audio is preferred Ability to influence architecture and engineering decisions across teams and explain complex technical trade-offs to technical and non-technical stakeholders Demonstrated ability to mentor senior engineers, lead technical reviews, and establish reusable engineering patterns and standards Proficiency in Microsoft Office Suite skills Show an ownership mindset in everything you do; be a problem solver, be curious and inspired to take action, be proactive, and seek ways to collaborate and connect with people and teams in support of driving success Maintain a continuous growth mindset through relationships with stakeholders, experts, colleagues, and mentors, as well as structured courses and programs Where applicable, fluency in English and languages relevant to the working market

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