Principal Software AI Engineer

Dun & Bradstreet

. Define the architecture and technical direction for enterprise AI/ML and GenAI capabilities, including agentic and conversational AI solutions using Vertex AI Agent Builder/ADK and Gemini .

Posted 10/28/2026full-timeHyderabad • IndiaLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and delivering enterprise-scale AI/ML solutions, with a strong focus on MLOps, cloud-native services, and AI governance. Proven ability to lead technical teams, mentor engineers, and influence architectural decisions across multiple teams.

Highest-signal resume keywords

AI/ML ArchitectureMLOps ImplementationVertex AI Agent BuilderPython Application DevelopmentCloud-Native Solutions on GCP

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI/ML SolutionsLarge Language ModelsRAG PipelinesVector Database IntegrationTool IntegrationEvent-Driven WorkflowsAPI DevelopmentModel EvaluationObservability PracticesIncident Management

Soft Skills

Technical LeadershipMentorshipCollaborationInfluencing DecisionsCommunication

Tools & Technologies

DockerKubernetesTerraformVertex AI PipelinesMLflowKubeflowFastAPIMicrosoft TeamsMicrosoft Graph APIHugging Face

Industry Keywords

AI GovernanceQuality StandardsSafety StandardsPerformance StandardsCost Management

Tech Stack

Tools & technologies

AWSCloudDockerGoogle Cloud PlatformKubernetesPythonTerraform

About the role

Key responsibilities & impact

Define the architecture and technical direction for enterprise AI/ML and GenAI capabilities, including agentic and conversational AI solutions using 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, 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 adoption of emerging AI tools and platforms
Provide training, guidance, and mentorship to engineers through code reviews, architecture reviews, knowledge sharing, and best-practice adoption
Perform additional duties as assigned

Requirements

What you’ll need

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
Where applicable, fluency in English and languages relevant to the working market