Architect

Avalara•UNAVAILABLE, UNAVAILABLE

12h

About The Position

We are looking for a Software Architect to define and drive the next stage of architecture for Avalara Tax Research (ATR), Avalara’s self-service tax research platform for rules, rates, taxability insights, and AI-assisted research experiences. This role exists now because ATR is expanding across indirect tax, direct tax, and trade-related research while also evolving AI-powered experiences through Avi and emerging MCP and AI agent patterns. The architect in this role will shape how ATR scales across product, platform, data, and AI-enabled experiences so the business can move faster without compromising reliability, trust, security, or performance. A successful candidate will combine deep software architecture expertise with strong product judgment. They should be comfortable driving long-range architecture, making pragmatic technology decisions, improving developer velocity, and ensuring ATR can operate securely and reliably at scale as the platform grows. How This Role Elevates Avalara Team This role elevates the team by creating stronger platform patterns, clearer technical direction, and better engineering leverage across ATR. The architect will reduce accidental complexity, address technical debt, improve design quality, and raise the technical bar for engineers and technical leads through coaching, architecture reviews, and reference patterns. Business This role elevates the business by enabling ATR to scale in a way that supports faster product delivery, better operational rigor, and a stronger AI-first platform foundation for ATR. Customer This role elevates the customer experience by improving the speed, reliability, and trustworthiness of ATR experiences that deliver tax answers, research workflows, and citation-backed insights. Better architecture should translate directly into more resilient user journeys, stronger performance, and faster access to high-confidence research outcomes.

Requirements

B.S. in Computer Science or Engineering, or equivalent practical experience.
10+ years of relevant work experience aligned to the scope and level of this role.
Significant experience designing and evolving SaaS systems in production.
Proven success as a Staff+ Engineer, Principal Engineer, Architect, or equivalent technical leader for business-critical platforms.
Deep experience with cloud-based service-oriented and distributed architectures, API design, asynchronous systems, and cloud-native platforms.
Strong understanding of search, retrieval, content-centric systems, metadata-driven architectures, and data modeling for complex domains.
Experience designing secure, highly observable systems with strong operational characteristics.
Experience with performance engineering, scalability planning, reliability patterns, and graceful degradation strategies.
Strong understanding of application security, identity, authorization, secrets management, and secure partner-facing integrations.
Demonstrated applied AI experience with AI-enabled product architectures, including retrieval-augmented systems, grounding and citations, prompt orchestration, model integration patterns, MCPs, and AI agents.
Excellent written and verbal communication skills, with the ability to influence across engineering, product, and executive stakeholders.
Strong judgment in balancing long-term architectural health with near-term product delivery.
Drive the ATR platform, engineering practices, and technical decisions stronger than they found them.
Raise the performance of others through coaching, feedback, and technical example.
Challenge assumptions respectfully and bring data into architectural tradeoff discussions.
Simplify complexity in ways that improve execution rigor and engineering leverage.
Hold a high standard for accountability to outcomes, not just activity.
You’ll bring experience using AI and AI-related technologies, ready to thrive here.
You’ll apply AI every day to business challenges - improving efficiency, contributing solutions, and driving results for your team, our company, and our customers.
You’ll grow with AI by staying curious about new trends and best practices, and by sharing what you learn so others can benefit too.

Responsibilities

Own and evolve the end-to-end architecture for ATR across application, data, integration, and platform layers.
Define scalable, secure, cloud-based architecture patterns for search, retrieval, content delivery, AI-assisted experiences, and export or sharing workflows.
Translate product strategy and business priorities into architecture roadmaps, technical investment areas, and phased execution plans.
Drive decisions on service boundaries, APIs, event-driven patterns, and platform interfaces that improve delivery speed and long-term maintainability.
Lead architecture for AI-native ATR capabilities, including grounded retrieval, citation-backed answer generation, MCP and AI agent access patterns, caching, proxying, and observability.
Establish architectural guardrails for security, privacy, compliance, resiliency, tenancy, and data governance in partnership with Security and SRE.
Improve developer leverage by standardizing reusable patterns, reference implementations, and design review practices.
Partner with tax domain stakeholders to make expert-curated tax content easier to ingest, model, retrieve, and serve with high trust.
Mentor engineers and technical leads, raising architectural and engineering maturity across the ATR organization.
Guide proof-of-concepts, production-readiness decisions, and major architecture tradeoffs in areas that materially affect scale, reliability, or time to market.
Drive measurable improvements in platform reliability, latency, and engineering delivery speed.
Designing AI-enabled architecture that improves retrieval quality, answer grounding, citation fidelity, and platform trust.
Applying AI responsibly and securely, in architecture decisions, including governance, access control, and risk-aware design.
Using AI to accelerate technical analysis, architecture exploration, and engineering productivity in ways that improve outcomes rather than convenience alone.
Identifying AI opportunities that improve customer experience, engineering leverage, operational efficiency, or product scale.
Raising the AI capability of the broader engineering organization by sharing effective patterns and practical best practices.