Avalara is accelerating an AI-first transformation in which agentic systems will change how customers, partners, and employees complete tax compliance work. This role exists now to help build and scale the Avalara Avi Agent (AAA), the agentic engineering foundation required for Aviator and future agentic capabilities across Avalara. The person in this role will turn emerging AI patterns into secure, reliable, production-grade systems that increase automation, improve customer outcomes, and enable Avalara teams to build agentic solutions faster and with higher quality.

How This Role Elevates Avalara

As an Agentic and Machine Learning Engineer V, you will raise Avalara's ability to deliver AI-powered products at enterprise scale. Your work will help establish reusable platform capabilities, implementation patterns, evaluation practices, and operational standards for agentic applications. By improving the reliability, accuracy, security, and scalability of these systems, you will enable faster delivery of Aviator and other agentic efforts while reducing duplicated engineering effort across teams. The impact of this role will be visible in stronger customer experiences, more efficient compliance workflows, and a higher bar for AI engineering across Avalara.

• Design, build, and operate foundational agentic platform capabilities that enable Aviator, AAA, and other Avalara agentic experiences to move from prototype to production.
• Develop scalable LLM application frameworks, orchestration patterns, tool integrations, and agent workflows that support enterprise-grade reliability, observability, security, and maintainability.
• Create and improve evaluation methods for agentic outcomes, including quality, accuracy, latency, cost, safety, and task-completion effectiveness.
• Translate ambiguous business and product needs into technical designs, prototypes, production features, and measurable engineering outcomes.
• Apply modern software engineering practices, including CI/CD, automated testing, code review, documentation, and operational readiness, to ensure high-quality delivery.
• Partner with product, engineering, security, data, and business stakeholders to ensure agentic capabilities solve meaningful customer and operational problems.
• Research, assess, and responsibly apply emerging AI technologies, including LLMs, model-context protocols, agent-to-agent patterns, retrieval, evaluation, and automation techniques.
• Document reusable patterns and implementation guidance that help Avalara engineers and software agents build consistently, safely, and efficiently.
• Mentor engineers and raise the technical bar through design reviews, code reviews, coaching, and examples of high-ownership execution.
• Strengthen the operational robustness of mature high-availability systems while introducing new AI capabilities without compromising customer trust or production stability.

12-Month Success Signals

• Aviator and other priority agentic initiatives are using production-grade platform capabilities owned or significantly shaped by this role.
• Reusable frameworks, libraries, or service patterns reduce the time required for Avalara teams to build and launch agentic workflows.
• Agentic systems have measurable evaluation coverage for quality, accuracy, reliability, cost, latency, and safety, with improvement trends visible over time.
• Critical agentic services meet agreed operational readiness standards, including observability, incident response, testing, documentation, and scalability targets.
• Stakeholders can point to measurable business or customer impact, such as increased automation, faster workflow completion, improved experience quality, or reduced manual effort.
• The engineer has raised team capability through mentoring, clear technical direction, stronger code/design review practices, and documented patterns that others reuse.

AI Expectations

Avalara is an AI-first company. This role must demonstrate applied AI capability, not casual tool usage. The person in this role is expected to embed AI into how engineering work gets done and to help others do the same responsibly.

• Use AI tools and agentic workflows to improve engineering speed, design exploration, testing, code quality, documentation, debugging, operational analysis, and knowledge reuse.
• Identify AI opportunities tied to measurable outcomes such as customer experience, workflow automation, developer productivity, reliability, cost efficiency, and risk reduction.
• Apply AI responsibly, securely, and ethically, including attention to data handling, governance, evaluation, auditability, and failure modes.
• Bring evidence to AI decisions, including impact metrics, tradeoff analysis, and clear reasoning for tool, model, protocol, or architecture choices.
• Share AI practices, prompts, evaluation methods, implementation patterns, and lessons learned to raise the AI capability of peers and partner teams.

Bar Raiser Expectations

This role is expected to raise the average performance of the team by improving technical judgment, execution quality, accountability, and business impact. The successful candidate will hold themselves and others to high standards, simplify complexity, challenge assumptions respectfully, use data in decision-making, and leave systems, processes, and teams stronger than they found them.

• B.S. in Computer Science, Engineering, or a closely related technical field.
• 8+ years of relevant professional experience building, deploying, and operating production software systems, with strong preference for Python experience.
• Hands-on experience building LLM applications, agentic systems, or AI-enabled workflows in production or production-like environments.
• Experience with LLMs such as GPT, Claude, Llama, or similar models, including practical understanding of prompting, orchestration, evaluation, and reliability considerations.
• Experience with enterprise-scale software design, distributed systems, data structures, design patterns, and high-availability system operations.
• Experience working in cloud computing environments such as AWS, Azure, or GCP.
• Applied familiarity with modern agentic integration patterns and protocols, such as MCP, A2A, tool use, retrieval, and multi-agent orchestration.
• Demonstrated ability to use AI to improve measurable outcomes, such as speed, quality, automation, insight, customer experience, or scale.
• Strong communication, documentation, mentoring, and cross-functional collaboration skills.

• 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.

Total Rewards

In addition to a great compensation package, paid time off, and paid parental leave, many Avalara employees are eligible for bonuses.