Machine Learning Engineer V

Role overview

Key responsibilities

• Lead architecture and delivery of agentic applications integrated with Avalara products, platforms, services, workflows, and user experiences.
• Build end-to-end AI-enabled systems with backend services, APIs, data access, orchestration, retrieval, tool integration, evaluation, deployment, monitoring, and support.
• Design reusable cloud-native platform capabilities for new compliance-content types.
• Improve scalability, reliability, resiliency, observability, maintainability, security, operational efficiency, latency, throughput, and cost efficiency.
• Integrate agents with enterprise applications, data sources, workflows, and cloud-native services.
• Establish standards for code quality, CI/CD, testing, deployment automation, observability, incident response, and production operations.
• Improve model selection, routing, quality, latency, reliability, and spend across providers and model families.
• Mentor engineers, lead design reviews, and raise technical standards.

Required qualifications

• Bachelor's degree in Computer Science or Engineering.
• 12+ years of relevant software engineering, ML engineering, applied ML, generative AI, enterprise application, or production AI experience.
• Experience building scalable distributed production systems with cloud-native architecture and a major cloud platform.
• Experience building enterprise agentic applications using LLMs, tools, workflows, retrieval, APIs, and integration patterns.
• Strong Python, backend, API, distributed-systems, cloud-native, and full-stack engineering experience.
• Experience delivering and supporting production ML, LLM, or AI-enabled application systems.
• Hands-on experience with RAG, embeddings, prompt design, evaluation, classification, agent tool use, or language-model workflows.
• Strong system design and technical leadership skills, including mentoring and design reviews.

Technical capabilities

• Build secure, reliable, scalable, maintainable, resilient, and cost-efficient AI product systems.
• Apply end-to-end SDLC practices including requirements, architecture, design, implementation, testing, release planning, deployment, monitoring, incident response, and support.
• Use cloud-native systems, asynchronous or distributed workflows, observability, deployment automation, and production operations.

Benefits

• Compensation package, paid time off, paid parental leave, and potential bonuses.
• Location-dependent health, life, and disability insurance benefits.