AI Engineer

About Avalon Healthcare Solutions:

Avalon Healthcare Solutions is the nation’s leader in diagnostic intelligence, uniquely focused on transforming the role of diagnostic testing across the healthcare ecosystem. Our proprietary Diagnostic Insights Platform delivers evidence-based policies, curated lab networks, and real-time analytics that simplify complex diagnostics, accelerate innovation adoption, and optimize diagnostic investments.

Supporting over 30 health plans and 100 million members nationwide, Avalon partners with payers and providers to ensure diagnostic testing is performed appropriately, efficiently, and at the right time. Our flexible solutions span routine and genetic testing management, automated adherence, and end-to-end diagnostics support—driving measurable value, reduced waste, and improved clinical outcomes.

With unmatched scientific rigor, deep clinical expertise, and a performance-based model, Avalon is redefining how diagnostics power personalized care and healthcare value. You will be part of a team that shapes a new market and business. Most importantly, you will help Avalon to achieve its mission and improve clinical outcomes and health care affordability for the people we serve.

For more information about Avalon, please visit www.avalonhcs.com.

Avalon Healthcare Solutions is an Equal Opportunity Employer - Vet/Disability.

This position description is subject to change at any time. As determined by the company based upon business needs, an employee in this position may be required to perform duties and take responsibility for work other than as described in this document.

About the AI Engineer Position:

The AI Engineer is a senior technical contributor responsible for leading the design, development, and deployment of scalable AI-powered solutions that support Avalon’s business objectives. This position owns complex AI features and projects from concept through production, including evaluating models and approaches, building reliable pipelines and integrations, and ensuring solutions meet high standards for accuracy, performance, and responsible use.

While primarily focused on team objectives, the AI Engineer may also lead cross-functional AI initiatives, coordinating efforts, aligning timelines, and partnering with Product, Design, and Data teams to translate business challenges into practical, measurable AI applications. This position works closely with principal engineers and architects to help shape AI strategy and technical direction, contribute to architectural decisions, and ensure AI systems remain consistent, secure, maintainable, and scalable across the platform.

The AI Engineer serves as a key contributor to expanding AI expertise across the organization by mentoring employees, supporting adoption of AI tools and practices, providing code and model reviews, and promoting best practices related to prompt and model evaluation, data quality, observability, and responsible deployment. This role combines hands-on technical execution with technical leadership and influence, helping advance Avalon’s AI capabilities while fostering a collaborative and innovative engineering culture.

This position is eligible for remote work, but quarterly travel will be required to Avalon’s corporate office located in Tampa, Florida.

AI Engineer – Essential Functions and Responsibilities:

• Contribute to the design, technical direction, and architecture of AI-enabled solutions, collaborating with business and technical teams to build fit-for-purpose solutions that apply generative AI and agentic capabilities where they deliver measurable value.
• Applies expert software engineering skills across a polyglot stack, including Python, Rust, Node.js/TypeScript, and Java, selecting the right language and runtime for each problem, and independently designs and develops key services with a focus on continuous integration and delivery.
• Designs, builds, and operates agentic systems, including multi-step and multi-agent workflows, tool and function calling, Model Context Protocol (MCP) servers and integrations, retrieval-augmented generation (RAG) pipelines, and agent memory and orchestration patterns.
• Applies advanced prompt engineering and context engineering practices, such as system prompt design, structured outputs, few-shot examples, prompt chaining, and prompt versioning, to produce reliable, accurate, and cost-effective model behavior.
• Maps business processes a…