Machine Learning Engineer V

Avalara Technologies; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Avalara Technologies seeks a Senior Machine Learning Engineer V to lead design and delivery of enterprise-grade agentic applications and ML systems that integrate with Avalara’s products, platforms, and workflows.

The role owns complex AI systems from architecture through production, including full-stack integration, backend services, orchestration, evaluation, deployment, and observability. You will mentor engineers and drive engineering standards in a fast-paced, secure environment.

Qualifications

• Experience building scalable, distributed production systems using cloud-native architecture and at least one major cloud platform (AWS, Azure, or GCP).
• Experience building enterprise agentic applications that use LLMs, tools, workflows, retrieval, APIs, and application integration patterns.
• Experience integrating AI capabilities into production applications, backend services, or enterprise systems.
• Strong software engineering skills in Python and backend service design, distributed systems, and cloud-native platforms.

Responsibilities

• Lead the architecture and delivery of enterprise-grade agentic applications that integrate with Avalara products, Content Engineering platforms, services, workflows, and user experiences.
• Build end-to-end AI-enabled systems that include backend services, APIs, data access patterns, agent orchestration, retrieval, tool integration, UI components, evaluation, deployment, monitoring, and production support.
• Design reusable platform capabilities and cloud-native services that support new compliance content types and improve the compliance content lifecycle.
• Make architecture decisions that improve scalability, reliability, resiliency, observability, maintainability, security, and operational efficiency.
• Operationalize AI capabilities using modern orchestration and agentic frameworks to improve automation, accuracy, delivery speed, and system quality.
• Integrate agents with enterprise applications, data sources, workflow systems, and cloud-native services while maintaining secure and maintainable system boundaries.
• Establish engineering standards for software design, CI/CD, automated testing, deployment automation, observability, incident response, and production operations.
• Improve platform performance, latency, throughput, reliability, and cost efficiency at scale.
• Apply evaluation, monitoring, and deployment practices that improve AI-enabled system performance in production.
• Work across model providers and model families to improve model selection, routing, quality, latency, reliability, and spend.
• Partner with engineering, infrastructure, product, security, data, and domain teams to deliver measurable customer and business outcomes.
• Mentor engineers, lead design reviews, create reusable engineering patterns, and raise team capability in distributed systems, cloud engineering, software architecture, full-stack development, and responsible AI adoption.

Skills

Architecture design

Distributed systems

Python

Cloud-native platforms

Backend services

Full-stack development

ML systems production

LLMs and AI tooling

Security and reliability

Mentoring engineers

Education

B.S. in Computer Science or Engineering

Job description

What You'll Do

Avalaras Content Engineering platform supports the compliance content that helps customers manage complex obligations across jurisdictions and product lines. Avalara is modernizing the compliance content lifecycle by building agentic scalable, reliable, cloud-native systems that make it faster and easier to onboard, validate, enrich, and deliver new content types.

This role exists to lead the design and delivery of enterprise-grade agentic applications and machine learning systems that integrate into Avalara’s products, platforms, and internal workflows. The Machine Learning Engineer V owns complex AI systems from architecture through production, including full-stack application integration, backend services, agent orchestration, model and agent evaluation, deployment, observability, support, and ongoing improvement.

This is not a pure machine learning operations role. This role requires a senior engineer who builds agentic systems, integrates them with mainstream applications, applies sound software development life cycle practices, and operates production AI capabilities as secure, reliable, scalable product systems.This role sits within Content Engineering team and you will be reporting to Senior Manager, ML Engineering.

What Your Responsibilities Will Be

• Lead the architecture and delivery of enterprise-grade agentic applications that integrate with Avalara products, Content Engineering platforms, services, workflows, and user experiences.
• Build end-to-end AI-enabled systems that include backend services, application programming interfaces, data access patterns, agent orchestration, retrieval, tool integration, user-facing application components, evaluation, deployment, monitoring, and production support.
• Design reusable platform capabilities and cloud-native services that support new compliance content types and improve the compliance content lifecycle.
• Make architecture decisions that improve scalability, reliability, resiliency, observability, maintainability, security, and operational efficiency.
• Operationalize AI capabilities using modern orchestration and agentic frameworks to improve automation, accuracy, delivery speed, and system quality.
• Integrate agents with enterprise applications, data sources, workflow systems, and cloud-native services while maintaining secure and maintainable system boundaries.
• Establish engineering standards for software design, code quality, continuous integration and delivery, automated testing, deployment automation, observability, incident response, and production operations.
• Improve platform performance, latency, throughput, reliability, and cost efficiency at scale.
• Apply evaluation, monitoring, and deployment practices that improve AI-enabled system performance in production.
• Work across model providers and model families to improve model selection, routing, quality, latency, reliability, and spend.
• Partner with engineering, infrastructure, product, security, data, and domain teams to deliver measurable customer and business outcomes.
• Mentor engineers, lead design reviews, create reusable engineering patterns, and raise team capability in distributed systems, cloud engineering, software architecture, full-stack development, and responsible AI adoption.

What You’ll Need To Be Successful

• B.S. in Computer Science or Engineering.
• 9+ years of experience in software engineering, machine learning engineering, applied machine learning, generative AI, enterprise application development, or production AI systems.
• Experience building scalable, distributed production systems using cloud-native architecture and at least one major cloud platform, such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform.
• Experience building enterprise agentic applications that use large language models, tools, workflows, retrieval, application programming interfaces, and application integration patterns.
• Experience integrating AI capabilities into production applications, backend services, user workflows, developer platforms, or enterprise systems.
• Strong software engineering skills in Python and experience designing backend services, application programming interfaces, distributed systems, and cloud-native platforms.
• Full-stack engineering experience across backend services, application programming interfaces, relational databases, scalable data access patterns, frontend-backend contracts, authentication, and modern web technologies.
• Experience applying the software development life cycle end to end, including requirements analysis, architecture, design, implementation, testing, release planning, deployment, monitoring, incident response, support, and continuous improvement.
• Experience shipping and supporting production machine learning, large language model, or AI-enabled application systems beyond experimentation or research prototypes.
• Hands‑on experience with retrieval‑augmented generation, embeddings, prompt design, evaluation, classification, agent tool‑use patterns, small language models, or large language model‑powered workflows.
• Experience with cloud-native systems, asynchronous or distributed workflows, observability, monitoring, deployment automation, and production operations.
• Ability to design for security, access control, governance, compliance, reliability, maintainability, resiliency, and cost efficiency.
• Strong system design skills and ability to lead technically complex initiatives across multiple stakeholders.
• Ability to mentor engineers, lead design reviews, and raise technical standards across teams.