Principal Software AI Engineer (R-20013)
Dun & Bradstreet1 day ago
Hyderābād, IndiaStaff+
Responsibilities
- Define architecture and technical direction for enterprise AI/ML and GenAI capabilities, including agentic and conversational AI solutions.
- Design and deliver production-grade LLM applications using embeddings, RAG pipelines, vector databases, tool use, orchestration, and multi-agent patterns.
- Establish reusable AI engineering standards, reference architectures, design patterns, and governance controls.
- Lead end-to-end MLOps capabilities for experimentation, evaluation, validation, deployment, monitoring, and lifecycle management.
- Architect scalable cloud-native AI services on GCP and Kubernetes using Docker, Terraform, and event-driven integrations.
- Lead data pipelines, APIs, and enterprise integrations that embed AI/ML into business applications and collaboration platforms.
- Define evaluation approaches and guide monitoring, incident response, root-cause analysis, and prevention for production AI systems.
- Lead technical design reviews, proofs of concept, technology evaluations, and build-versus-buy decisions.
- Mentor engineers through training, code reviews, architecture reviews, knowledge sharing, and best-practice adoption.
Requirements
- Bachelor's degree in computer science, engineering, or a related field; a master's degree is preferred.
- 12+ years of relevant software engineering experience, including substantial experience architecting and deploying production AI/ML solutions at enterprise scale.
- Proven technical leadership designing and delivering large-scale distributed AI systems across multiple teams or products.
- Deep hands-on experience with Vertex AI Agent Builder/ADK, Gemini, agentic AI frameworks, and large language models.
- Expertise in Python application development and APIs, including FastAPI, software design, testing, code review, and version control.
- Experience designing LLM applications involving RAG pipelines, embeddings, vector databases, tool integration, orchestration, prompt and context engineering, and model evaluation.
- Deep experience with GenAI ecosystems such as LangChain and Hugging Face.
- Experience designing end-to-end MLOps capabilities using Vertex AI Pipelines, MLflow, or Kubeflow.
- Advanced experience architecting cloud-native solutions on GCP; AWS AI/ML services experience is a plus.
- Experience deploying and operating scalable AI/ML services using Docker, Kubernetes, Terraform, observability, and incident-management practices.
- Experience defining AI quality, safety, security, privacy, governance, performance, reliability, and cost-management standards.
- Experience with event-driven workflows, enterprise data pipelines, Microsoft Teams, and Microsoft Graph API.
- Multimodal AI experience spanning text, image, and audio is preferred.
- Ability to influence technical decisions, explain complex trade-offs, mentor senior engineers, and lead technical reviews.
- Proficiency with Microsoft Office Suite and, where applicable, English and languages relevant to the working market.
Tech Stack
Categories
About Dun & Bradstreet
Dun & Bradstreet builds data and analytics products that help companies assess credit, manage third‑party risk and compliance, and find customers. Its Data Cloud, business credit reports, D‑U‑N‑S Number identity, and APIs are sold as subscriptions and software to finance, procurement, and go‑to‑market teams. Founded in 1841, the company is headquartered in Jacksonville, Florida, and is publicly traded on the NYSE (ticker: DNB).