What You'll Do
Avalara’s 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. This is a #LI-Remote role.
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 current compliance content types and improve the compliance content lifecycle.
• Make architecture decisions that improve scalabili
📌 Machine Learning Engineer V (India)
🏢 Avalara
📍 India