Principal Software AI Engineer (R-20013)
Dun & Bradstreet, Inc. (company site, opens in a new tab)12+ yrsHyderabadHybridDirect career pageEst. 40–70 LPA
Posted 8h ago
Reach people at Dun & Bradstreet, Inc.
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Dun & Bradstreet, Inc. you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Define architecture and technical direction for enterprise-scale AI/ML and Generative AI solutions including agentic and conversational AI using Vertex AI Agent Builder/ADK and Gemini.
- Lead design and delivery of production-ready large language model applications involving embeddings, RAG pipelines, vector database integration, orchestration, and multi-agent patterns.
- Establish AI engineering standards, governance, MLOps/CI-CD capabilities, cloud-native AI services on GCP and Kubernetes, and guide integration of AI/ML into business applications.
Minimum Requirements
- Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
- 12+ years of relevant software engineering experience with substantial enterprise AI/ML architecture and deployment.
- Proven technical leadership in large-scale, distributed AI systems and deep hands-on experience with Vertex AI Agent Builder/ADK and Gemini large language models.
- Advanced expertise in Python development, MLOps tools (Vertex AI Pipelines, MLflow, Kubeflow), cloud-native GCP solutions, Docker, Kubernetes, Terraform, and AI governance standards.
Ideal Candidate Profile
- Experienced leader in architecting and delivering complex, scalable AI/ML systems across multiple products or teams in enterprise environments.
- Strong expertise in agentic AI frameworks, LLM-based applications, GenAI ecosystems (LangChain, Hugging Face), and production AI quality, safety, security, and cost metrics.
- Skilled in influencing architecture/engineering decisions, mentoring senior engineers, and establishing enterprise-wide AI engineering best practices.