Job Description
Lirio is a technology/software company that provides expertise in a variety of behavioral science domains (e.g., behavioral economics, social psychology, public health), data science, and machine learning to drive consumer engagement, close gaps in preventive and chronic care, and promote health and well-being across an individual’s lifespan. Lirio’s behavior change AI platform unites behavioral science with advanced artificial intelligence (AI) to deliver Precision Nudging health interventions. Precision Nudging is the application of behavioral science to health interventions personalized by AI to each individual that overcome barriers to action at the right time and place for scalable, behavior change.
This is a remote role with the opportunity to be hybrid if located in Tennessee. All applicants must be authorized to work in the US without sponsorship.
To ensure an excellent onboarding experience and integration into the company, new colleagues will spend their first week onsite at one of our offices in Tennessee. Travel expenses will be paid. This is a requirement.
Position Summary
The Senior AI Platform Engineeris responsible for architecting, building, and maintainingthe agentic software execution layer within Lirio’s Precision Nudging Platform. This position bridges the gap between MLOps and autonomous AI orchestration by engineering the workflows for AI agents to operate securely and reliably. Key components of this role are AI workflow design, managing and optimizingthe AI infrastructure and life cycle, assuring interoperability with Lirio Platform systems and client agents, and safety/governance of implemented AI systems. The Senior AI Platform Engineer will collaboratein a cross-functional team that includes platform, cloud, security and machine learning research engineers, as well as system architects.
Essential Duties & Responsibilities
Agent Orchestration & Workflow Design
- Design and implement infrastructure to support LLM-based autonomous agents capable of multi-step reasoning, planning, and task execution.
- Build and manage directed workflows using state machines and tools to coordinate complex AI-human handoffs.
- Lead the architectural design and technical implementation of interoperability standards that enable seamless communication between autonomous agents and diverse software ecosystems
AI Infrastructure & Lifecycle
- Architect and maintain cloud-native platforms that support end-to-end AI workflows, from model experimentation to high-availability production deployment.
- Develop evaluation frameworks and observability dashboards to monitor agent accuracy, latency, cost, and safety guardrails.
- Optimize agent performance by managing tool discovery and context window efficiency through standardized protocols, ensuring agents can dynamically access and execute the right capabilities on-the-fly.
Governance & Security
- Embed healthcare regulatory compliance (e.g. HIPAA) directly into the platform layer through automated guardrails and audit trails.
- Implement security controls against prompt injection and ensure PII/PHI de-identification within agentic data flows.
- Implement secure authentication, role-based access controls, and data masking within interoperability layers to serve as a secure gatekeeper between AI agents and sensitive enterprise systems.
Engineering Support & Technical Leadership
- Provide subject matter expertise and technical support to engineering teams during implementation.
- Build prototypes, reference integrations, or proof-of-concept solutions to validate design decisions and de-risk complex implementations.
- Evaluate existing systems and propose improvements or replacements.
- Promote AI-assisted engineering tools and modern development practices consistent with Lirio’s engineering culture.
- Document processes, designs, implementations, and best practices for future reference.
Cross-Functional Collaboration
- Serve as a contributing member of Lirio’s Architecture Team, helping to maintain architectural coherence and platform quality.
- Partner with Product Management to shape solution approaches before work enters development planning and execution.
- Work closely with Cloud, Data, AI/ML, Behavioral Science, and Engineering teams to ensure solutions support personalization, scalability, and measurable outcomes.
- Participate in the Engineering Council, helping to define and uphold engineering standards, patterns, and technical governance.
Incident response
- Diagnose and respond to issues in the implementation of agent orchestrations, adjusting guardrails and workflows as needed.
Basic Qualifications
- 3–5+ years of experience in healthcare technology, with a deep understanding of clinical workflows, Electronic Health Record (EHR) integration (e.g., Epic, Cerner), and HL7/FHIR data standards.
- Bachelor’s degree in related field preferred.
- Proven track record of building cloud-native autonomous agent systems in regulated environments, including the implementation of safeguards for direct patient/member interaction.
- Extensive experience in LLMOps/MLOps, specifically managing the transition of agentic prototypes into production-grade healthcare applications.
- Hands-on experience with LLM APIs, AI coding tools (cursor, co-pilot, claude code, etc) and orchestrations frameworks
- Strong understanding of compliance requirements in regulated environments (HIPAA/HITRUST).
- Ability to design, implement, and maintain complex automation and agent workflows.
- Experience with security, audit, and risk mitigation in software delivery.
- Programming Languages: Expert proficiency in Python (primary for AI) and C# or Java (for enterprise integration).
- Azure AI Services: Hands-on experience with Azure OpenAI Service, Azure AI Agent Service, and Azure Machine Learning (Azure ML).
- Orchestration & Data Tools: Proficiency with LangChain, Microsoft Semantic Kernel, and Databricks and/or Snowflake.
- Interoperability Protocols: Deep hands-on experience with emerging agent communication standards, specifically the Model Context Protocol (MCP), including proficiency with its SDKs (Python, TypeScript, or Go).
- Infrastructure & DevOps: Advanced skills in Terraform for Infrastructure as Code (IaC), Docker, Kubernetes (AKS), and Azure DevOps (ADO) for CI/CD.
- Vector Databases: Experience with Pinecone, Weaviate, or Azure AI Search for high-dimensional data retrieval.
- Experience in healthcare, fintech or other regulated industries.
- Prior work with Model Context Protocol (MCP) or similar integration standards.
- Familiarity with muli-model AI routing and bench-marking.
- Demonstrated ability to lead platform adoption and drive organizational change.
Benefits
- Medical (HSA available)
- Dental
- Vision
- Short-term & long-term disability (company-paid)
- Life & AD&D (company-paid)
- 401K with company match
- 10 paid holidays, quarterly company closure dates, + holiday week company closure
- Flexible time off policy
- Work from home
- 6 weeks paid parental leave
- Salary range: $165k-$185k
