Principal Software AI Engineer (R-20013)

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