DE&A AI Engineer

JD:

About the Role

As an Applied AI Developer on the Enterprise AI team youll evaluate where AI fits build the tools and platforms that make it practical and enable teams across Rubrik to adopt modern AI development patterns including LLM orchestration agentic workflows and model governance. You stay hands-on and outcomes-oriented: prototyping evaluating emerging AI technologies and shipping solutions that make the organization measurably more efficient.

What Youll Do

• Think AI-first assess where agentic approaches genuinely outperform conventional solutions then own the quality bar: build automated evals simulation tests and regression frameworks that keep our AI systems reliable and improving as they scale.
• Design agentic systems tool orchestration agent reasoning memory MCP integrations and human-in-the-loop workflows.
• Define and implement AI governance patterns guardrails data lineage auditability and responsible AI practices that ensure our agentic systems are safe compliant and trustworthy.
• Drive adoption through pilots proofs-of-concept and scalable implementations across engineering teams.
• Collaborate with various business functions product security and platform teams to translate AI use cases into production-grade end-to-end solutions.

What Were Looking For

• 5 years of professional software engineering with at least 2 years focused on applied AI in production systems.
• Proficient in Python and/or Go; comfortable reading and writing in the other.
• Proven experience building and scaling multi-agent or agent-driven systems in production real-world operational ownership not just simple LLM workflows.
• Hands-on experience with modern agent ecosystems including frameworks (e.g. LangGraph Google ADK Mastra Claude Agent SDK) observability and evals tooling (e.g. Langfuse LangSmith Braintrust) MCP implementations and leading AI SDKs (e.g. OpenAI Anthropic).
• Strong systems and backend architecture fundamentals designing scalable reliable systems and handling infrastructure performance failure modes cost and deployment concerns.
• Good understanding of cloud-native environments (GCP and/or AWS) compute storage networking and managed AI services.
• Experience designing and integrating with enterprise APIs (REST GraphQL) including authentication and authorization patterns (OAuth2 SAML API keys RBAC). Comfortable working with backend databases (SQL and NoSQL) writing queries understanding data models and building data access layers that enforce role-based access control.
• Strong cross-functional collaborator and communicator able to partner with Product Operations and domain experts to deliver end-to-end systems with measurable real-world impact.
• A force-multiplier on the team you raise the bar for clarity of thinking system design standards and team execution.

Nice to Have

• Experience with AI evaluation tooling (Langfuse LangSmith Braintrust or custom eval frameworks).
• Experience building custom MCP servers not just consuming them.
• Familiarity with containerization and orchestration (Docker Kubernetes).
• AI-native builder with high velocity and ownership intellectual curiosity rapid adoption of new tools bias to action and the ability to drive ambiguous problems from concept to production.
• Hands-on experience with inference cost optimization managing spend as agent deployments scale.
• Experience using AI-powered coding agents (e.g. Claude Code GitHub Copilot Cursor Windsurf) to accelerate development workflows rapid prototyping code generation debugging and test writing.
• Experience with RAG (Retrieval-Augmented Generation) architectures and document retrieval pipelines vector databases embedding models chunking strategies and hybrid search for building agents that answer questions grounded in enterprise documentation.