AI Solution Architect

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.

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.

Key taska:

- 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.