About the role
The Solution Architect designs AI-driven, LLM‑based technical solutions across multiple pods. This role is responsible for interpreting business requirements, shaping the architectural approach, and ensuring that solutions follow emerging standards-while moving quickly enough to support the high-volume demand.
Our expectations
- 4-10 years experience in architecting or engineering AI‑powered systems:
- LLM-based applications
- Retrieval‑augmented generation (RAG)
- Vector databases
- Agentic workflows
- Python-based AI stacks (LangChain, LangGraph, etc.)
- Ability to design flexible and modular architectures, not monolithic products.
- Experience reviewing and validating multi-component solutions (data ingestion, ETL, AI layer, UI/UX where needed).
Soft Skills
- Strong problem-solving mindset with ability to propose pragmatic solutions quickly.
- Comfortable working across time zones with U.S. architects and partners.
- Curious, collaborative, and willing to share learnings in a community-of-practice setting.
- Ability to support multiple pods simultaneously with consistent quality.
- High technical credibility and ability to challenge & guide developers.
Main responsibilities
- Design end-to-end architectures for GenAI/LLM-based solutions across 100+ use cases.
- Review requirements & business architecture documents and translate them into actionable solution designs.
- Collaborate closely with TPMs and engineering teams to guide implementation, troubleshoot risks, and ensure alignment.
- Create scalable, repeatable solution patterns, allowing reuse of components across pods and industries. (E.g., reusable release‑management agents across SAP/Oracle/Guidewire.)
- Balance speed over formality early on, while gradually contributing to future standardization across teams.
- Support multi-shipment planning, contributing to feasibility assessments (POC -> MVP).
- Participate in an architecture guild/committee as standards mature.
