Software Development Engineer V

What You'll Do

Avalara is accelerating its AI-first transformation by building intelligent systems that automate complex compliance workflows, understand unstructured and structured data, and enable product teams to deliver AI-powered experiences at scale. As Avalara works toward its ambition of being part of every transaction in the world, we are investing in a Senior Tech Lead to build the next generation of production AI capabilities.

Design & Architecture

• Lead the end-to-end design of complex, distributed features — from data flow and API contracts through to deployment and operational observability.
• Apply proven design principles (SOLID, domain-driven design, event-driven architecture, 12-factor apps) to ensure systems are scalable, maintainable, and resilient.
• Define and document architecture decisions clearly, with well-reasoned trade-offs that the team and stakeholders can understand and act on.
• Design multi-tenant, cloud-native services that handle high classification throughput with low latency and strong fault tolerance.

Agentic AI Engineering

• Build and operate agentic AI workflows — multi-step pipelines where LLM-based
  agents reason over product descriptions, retrieve external evidence (web content, trade
  documents, regulatory data), and deliver classification decisions with calibrated
  confidence.
• Implement retrieval-augmented generation (RAG) pipelines using vector databases to
  ground LLM reasoning in authoritative customs and trade reference data.
  Design human-in-the-loop orchestration — intelligent escalation logic that determines
  when an AI agent can classify autonomously versus when it should route to a human
  expert, with transparent confidence signals and audit trails.
• Build and extend MCP (Model Context Protocol) server capabilities — exposing
  classification APIs as tools that AI agents and partner platforms can discover and invoke
  autonomously, enabling agentic compliance workflows inside ERPs and marketplace
  systems.
• Evaluate, fine-tune, and govern LLM-based components in production — managing
  prompt engineering, output validation, hallucination mitigation, and responsible AI
  practices.

What Your Responsibilities Will Be

Full-Stack Engineering

• Build high-quality backend services and APIs in Java (Spring Boot) and Python
  (FastAPI) that handle large-scale product classification intake, orchestration, and
  delivery.
• Design and maintain asynchronous processing pipelines using message queues (e.g.,
  AWS SQS) and batch frameworks to handle classification volumes ranging from dozens
  to millions of items per customer.
• Develop or contribute to frontend components and internal tooling where needed to
  complete features end-to-end.
• Work confidently with relational databases (MySQL, PostgreSQL) and vector stores for
  both transactional and semantic retrieval workloads.

Platform & Operations

• Deploy and operate services on Kubernetes (AWS EKS) — writing Helm configurations,
  defining resource policies, and ensuring services meet availability and latency SLOs.
• Build observability into every service — structured logging, distributed tracing, and
  dashboards that make system behavior transparent in production.
• Own incident response for classification services — perform root cause analysis,
  implement durable fixes, and document post-incident learnings.
• Embed security best practices (secrets management, least-privilege IAM, OWASP
  principles) across all services.

Team & Engineering Culture

• Conduct high-quality code and design reviews — providing specific, constructive
  feedback that raises technical standards and accelerates the growth of junior engineers.
• Mentor I3 and I4 engineers through pairing sessions, technical coaching, and structured
  knowledge transfer.
• Proactively identify and address technical debt before it compounds — proposing and
  leading refactors that improve long-term velocity.
• Champion engineering best practices: CI/CD discipline, automated testing,
  documentation, and clear Definition of Done.

Elevate the team:

• Share agentic workflow patterns, prompt engineering techniques, and AI tool
  recommendations with the broader engineering team.
• Identify new agentic use cases within the product roadmap and advocate for their
  prioritization with engineering and product leadership.
• Help junior engineers go from "AI-aware" to "AI-effective" through hands-on
  demonstration and coaching.

What You’ll Need To Be Successful

Required Qualifications

• Bachelor's degree in Computer Science
• Minimum 10 years of professional software engineering experience, with a
  demonstrated track record of senior-level technical ownership and impact.
• Strong hands-on experience designing and building distributed backend systems —
  RESTful APIs, microservices, asynchronous messaging, and event-driven architectures.
• Proficiency in at least one of Java (Spring Boot) or Python (FastAPI / asyncio), with
  the ability to read and contribute across both.
• Hands-on experience integrating LLMs or ML models into production systems —
  including prompt design, output validation, latency management, and model
  observability.
• Experience deploying and operating services on cloud infrastructure (AWS preferred)
  using containerization and orchestration tools (Docker, Kubernetes).
• Strong understanding of software design principles — SOLID, clean architecture,
  domain-driven design, and design for scalability and resilience.
• Experience building and enforcing automated testing strategies — unit, integration,
  and end-to-end testing — with a disciplined approach to test coverage.
• Demonstrated ability to adapt to new technology stacks — comfortable picking up
  unfamiliar languages, frameworks, or tooling when required by the problem.
• Demonstrated, hands-on AI usage that has produced measurable improvements in
  engineering or product outcomes — this is a required qualifier, not a nice-to-have.
• Strong written and spoken English communication skills for effective collaboration with
  globally distributed engineering, product, and business teams.

AI Bar Raiser

This role carries explicit Agentic AI expectations.

Casual or passive AI use is not sufficient —
AI must materially change what you and the team can deliver.
Accelerate your own engineering:

• Use AI-assisted coding tools (GitHub Copilot, Cursor, or equivalent) in your daily
  workflow and demonstrate measurable gains — reduced cycle time, higher test
  coverage, faster code reviews, or improved documentation quality.
• Apply LLMs to accelerate design documentation, runbook authoring, API specification
  drafting, and code review commentary.
• Critically evaluate all AI-generated outputs and apply sound engineering judgment — AI
  assists; you own the result.

Build agentic product capabilities:

• Design classification agents that can autonomously plan, retrieve, reason, and decide —
  going beyond single-step ML inference to full reasoning loops.
  Implement responsible, explainable AI — every classification decision should carry a
  confidence signal, a rationale trail, and a graceful human-escalation path.
• Govern LLM behavior in production — prevent prompt injection, manage output
  variability, monitor for drift, and apply security-conscious AI practices.

Bar Raiser Expectations
At Avalara, we hire only Bar Raisers — individuals who make the team permanently better by
their presence. This is not about tenure or title; it is about raising the standard of what the team
delivers and how it delivers it.

As an I5 engineer on this team, you will:

• Hold a high bar for design and code quality — own every system you touch, and leave
  it better than you found it.
• Bring data and reasoning into technical discussions — opinions backed by evidence,
  trade-offs clearly articulated.
• Challenge and be challenged — contribute to healthy engineering debate and maintain
  intellectual humility when better approaches emerge.
• Make AI a team habit, not a personal trick — share what works, teach others, and
  drive measurable AI adoption across the team.
• Grow the engineers around you — the team's output is your output.
  If a candidate cannot demonstrate that their work has materially improved the quality, speed, or
  scale of what their team delivers, they do not meet the Bar Raiser standard for this level.

12-Month Success Signals

• Delivered at least one production-grade agentic AI capability — such as an autonomous
  classification agent that retrieves product evidence and improves coverage — with
  measurable accuracy improvement.
• Led the design and delivery of one significant architectural improvement to the
  classification platform (e.g., a new intake integration, a new API layer, or a reliability
  upgrade).
• Measurably improved engineering velocity for the India team through AI-assisted
  development practices — demonstrable through cycle time, test coverage, or deployment
  frequency metrics.
• Contributed to at least one MCP server tool expansion enabling external AI agents or
  partner platforms to consume classification capabilities.
• Grown at least two junior engineers through regular mentoring, with documented
  improvements in their scope of ownership and delivery quality.
• Established or improved at least one team engineering standard (testing, code review,
  observability, or architectural

Avalara is an AI-first Company

AI is embedded in our workflows, decision-making, and products. Success here requires embracing AI as an essential capability.

• 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.

How We’ll Take Care Of You

Total Rewards

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

Health & Wellness
Benefits vary by location but generally include private medical, life, and disability insurance.

Inclusive culture and diversity
Avalara strongly supports diversity, equity, and inclusion, and is committed to integrating them into our business practices and our organizational culture. We also have a total of 8 employee-run resource groups, each with senior leadership and exec sponsorship.

What You Need To Know About Avalara

We’re defining the relationship between tax and tech.

We’ve already built an industry-leading cloud compliance platform, processing over 54 billion customer API calls and over 6.6 million tax returns a year. Our growth is real - we're a billion dollar business - and we’re not slowing down until we’ve achieved our mission - to be part of every transaction in the world.

We’re bright, innovative, and disruptive, like the orange we love to wear. It captures our quirky spirit and optimistic mindset. It shows off the culture we’ve designed, that empowers our people to win. We’ve been different from day one. Join us, and your career will be too.

We’re An Equal Opportunity Employer

Supporting diversity and inclusion is a cornerstone of our company — we don’t want people to fit into our culture, but to enrich it. All qualified candidates will receive consideration for employment without regard to race, color, creed, religion, age, gender, national orientation, disability, sexual orientation, US Veteran status, or any other factor protected by law. If you require any reasonable adjustments during the recruitment process, please let us know.