The Software Architect owns technical architecture for the Gen AI Platform, including design decisions that support agentic capabilities, governance features, and product integrations. This role combines hands-on engineering with technical leadership to guide scalable, cloud-native solutions across products.
A Typical Day in the Life Includes:
· Define target architecture for agentic services built on AWS, Anthropic Claude API/MCP, Python services, and Angular-based front ends.
· Write and review code, evaluate designs, and prototype technical approaches for complex platform needs.
· Establish standards for integrating agentic frameworks such as LangChain, LangGraph, or comparable tools into production services.
· Partner with AI Governance engineering to incorporate explainability, observability, and prevention capabilities into the platform architecture.
· Review technical designs from engineering teams and guide decisions related to data flow, service boundaries, and security posture.
· Serve as a technical escalation partner for platform-level risks and architecture decisions.
· Collaborate across engineering, product, and governance teams to support reliable, scalable, and responsible Gen AI platform capabilities.
Basic Qualifications:
· Experience architecting distributed, cloud-native systems in production environments.
· Experience writing and reviewing code in a hands-on technical architecture or engineering role.
· Experience using AI-assisted coding tools such as Claude Code, AWS Kiro, OpenAI Codex, or comparable tools.
· Experience with Python and ability to evaluate front-end architecture using Angular or comparable JavaScript frameworks.
· Working knowledge of AWS services, including compute, serverless, container orchestration, and managed ML/AI services.
· Exposure to agentic frameworks such as LangChain, LangGraph, or comparable frameworks.
· Experience setting technical direction across engineering teams or complex technical initiatives.
Preferred Qualifications:
· Experience architecting systems with governance, auditability, or explainability requirements.
· Familiarity with Anthropic Claude API and Model Context Protocol (MCP), or comparable LLM provider APIs and tool-use protocols.
· Experience designing services for reliability, cost efficiency, latency, and operational support.
· Experience guiding security, data flow, or service boundary decisions for platform services.
· Understanding of responsible AI practices, including evaluation of AI outputs and appropriate use of AI tools.
· Experience collaborating with product, governance, and engineering stakeholders on platform architecture.
· Experience supporting Gen AI, machine learning, or AI-enabled product capabilities in production environments.