As an Experienced AI Engineer (AI Agent), you will take ownership of building reliable AI agents for assigned use cases, with a focus on basic evaluation, monitoring, and production quality.
- Develop AI Agents for Product Use Cases
- Develop AI agents for use cases such as conversational Q&A, data retrieval (text-to-SQL / Data Platform querying), compliance review, data analysis, and insight generation - as well as new use cases emerging along the product roadmap.
- Build and optimize RAG pipelines, including chunking, embedding, vector search, reranking, and citation-based responses to ground agents in internal data and documentation and reduce hallucinations.
- Apply prompt engineering, function/tool calling, and structured output; integrate LLMs with APIs, databases, and internal tools, with MCP (Model Context Protocol) as a preferred standard.
- Test and validate AI agent accuracy, continuously optimize agent performance, and ensure safe operation in production environments.
- Production Deployment & Quality Assurance
Implement basic evaluation (eval), monitoring, and tracing for AI agents; apply existing guardrails such as out-of-scope query handling and authorization enforcement, while optimizing token usage and latency according to product requirements.
Translate business requirements into AI agent workflows with clear error handling and fallback mechanisms; collaborate with Analytics, Backend/Data, and Product teams to bring AI capabilities into production.
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As a Senior AI Engineer (AI Agent), you will design AI agent systems that maximize the value of a unified and interconnected data platform. You will define technical standards for multi-agent systems, semantic layers, and release-gating evaluation, ensuring that the platform is reliable, secure, and scalable for sensitive cybersecurity data.
As use cases continuously expand with user needs and the product roadmap, you will design a general-purpose and reusable agent platform - including shared tools/MCP, semantic layers, and evaluation frameworks - that enables rapid development of new use cases while maintaining quality. You will also provide technical leadership and mentorship to the AI engineering team.
- AI Agent Architecture & System Development
- Design end-to-end AI agent architectures for use cases such as conversational Q&A, Data Platform querying (text-to-SQL / semantic layer), compliance review, data analysis, and new use cases driven by the product roadmap.
- Design multi-agent workflows with agent handoffs, fallback mechanisms, and human-in-the-loop controls for irreversible or high-impact actions.
- Build a semantic layer with consistent definitions of metrics, entities, and data lineage to improve agent reasoning and query generation across the unified data platform; standardize reusable tools/MCP services across the platform.
- Build and evolve production-grade RAG systems, including hybrid search, reranking, retrieval quality optimization, and citation-based responses; establish data-driven mechanisms for continuously measuring and improving retrieval quality.
- Quality, Reliability & Security
- Build an evaluation framework that gates deployment, including golden datasets, model-graded evaluation, and regression suites integrated into CI pipelines; establish end-to-end observability and tracing.
- Optimize LLM usage, latency, and cost; design monitoring and usage-management mechanisms across AI agents.
- Design AI agents with protection against prompt injection, data leakage, unauthorized access, and other AI-specific security risks.
- Make architectural decisions around scalability and maintainability; evaluate and select technologies aligned with the system architecture and product requirements.
- Technical Leadership & Collaboration
- Provide technical leadership through mentoring Middle/Junior engineers, conducting code reviews, and collaborating with Product, Data, and DevOps teams to improve the team's engineering capabilities and delivery efficiency.