Principal Software AI Engineer (R-20013)

Shape the Future with Dun & Bradstreet At Dun & Bradstreet, we believe data has the power to create a better tomorrow. As a global leader in business decisioning data and analytics, we help companies worldwide grow, manage risk, and innovate. Since 1841, businesses have trusted us to turn uncertainty into opportunity. We’re a diverse, global team that values creativity, collaboration, and bold ideas. Are you ready to make an impact and help shape what’s next? Join us! Explore opportunities at dnb.com/careers.
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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