Applied AI Engineer

Map Tax workflows and determine where to apply AI, deterministic rules, automation, and human review.

• Build and maintain LLM assistants, document-intelligence solutions, and agentic automation capabilities for Tax.

• Develop agent orchestration using LLM APIs, tool calling, structured outputs, state management, and multi-agent design patterns.

• Design and implement RAG pipelines covering document ingestion, chunking, embeddings, vector search, retrieval, grounding, and source management.

• Build AI evaluation frameworks using reference datasets, automated tests, regression testing, quality metrics, monitoring, and feedback loops.

• Monitor AI solutions for quality, latency, cost, reliability, security, and production performance. Implement guardrails, grounding, access controls, traceability, human review, and responsible AI controls to reduce hallucinations and improve trust.

• Combine probabilistic AI with deterministic rules, validation logic, business controls, and auditability requirements. Build, deploy, and support secure AI services across cloud platforms, backend systems, and enterprise environments.

• Translate complex Tax requirements into reliable AI-enabled product features in collaboration with Tax specialists and engineering teams.

• Contribute to reusable AI frameworks, evaluation harnesses, agent tooling, technical documentation, and engineering standards.

• Participate in code reviews, automated testing, release validation, Git, and CI/CD practices to improve solution quality and reliability.

• Diagnose and resolve production issues across AI services, data pipelines, integrations, and Tax product modules.

• Communicate technical decisions, AI outputs, risks, limitations, and solution recommendations to technical and non-technical stakeholders.