Software Development Engineer V

Role Overview

Core Responsibilities

• Lead end-to-end design of distributed features and agentic AI workflows from data flows and API contracts through deployment and observability.
• Build backend services and APIs in Java with Spring Boot and Python with FastAPI.
• Design asynchronous processing pipelines using message queues such as AWS SQS.
• Deploy and operate cloud services on Kubernetes and AWS EKS with observability and embedded security practices.
• Develop multi-step AI pipelines using RAG, vector databases, and human-in-the-loop escalation.
• Build and extend Model Context Protocol server capabilities for classification APIs invoked by AI agents and partner platforms.
• Conduct code reviews, mentor junior engineers, and drive measurable AI adoption through coaching and shared workflow patterns.

Required Qualifications

• Bachelor's degree in Computer Science.
• Minimum 10 years of professional software engineering experience with senior-level technical ownership and impact.
• Extensive experience designing and building distributed backend systems, RESTful APIs, microservices, and event-driven architectures.
• Proficiency in Java with Spring Boot or Python with FastAPI and asyncio, with the ability to contribute across both stacks.
• Hands-on experience integrating LLMs or ML models into production systems, including prompt design, output validation, and responsible AI governance.
• Experience deploying and operating cloud infrastructure with containerization and orchestration tools such as Docker and Kubernetes; AWS is preferred.
• Demonstrated hands-on AI usage that has produced measurable engineering or product improvements.
• Exceptional communication skills and commitment to raising standards for design, code quality, and engineering debate.

AI Bar Raiser Expectations

• Use AI-assisted tools such as GitHub Copilot or Cursor daily to reduce cycle time, draft specifications, and improve code quality while owning the final result.
• Design classification agents with reasoning loops that autonomously plan, retrieve, and decide, with transparent confidence signals and human escalation.
• Secure production LLM behavior by mitigating prompt injection, monitoring drift, and applying responsible AI practices.

About Avalara

Total Rewards and Culture

• Compensation packages may include paid time off, paid parental leave, and bonuses for eligible employees.
• Benefits vary by location and generally include private medical, life, and disability insurance.
• Avalara supports diversity, equity, inclusion, and employee resource groups.