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.
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.
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.
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.
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.
Diagnose and respond to issues in the implementation of agent orchestrations, adjusting guardrails and workflows as needed.

Requirements

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.
Expert proficiency in Python (primary for AI) and C# or Java (for enterprise integration).
Hands-on experience with Azure OpenAI Service, Azure AI Agent Service, and Azure Machine Learning (Azure ML).
Proficiency with LangChain, Microsoft Semantic Kernel, and Databricks and/or Snowflake.
Deep hands-on experience with emerging agent communication standards, specifically the Model Context Protocol (MCP), including proficiency with its SDKs (Python, TypeScript, or Go).
Advanced skills in Terraform for Infrastructure as Code (IaC), Docker, Kubernetes (AKS), and Azure DevOps (ADO) for CI/CD.
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 multi-model AI routing and benchmarking.
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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

LLMOpsMLOpsPythonC#JavaTerraformDockerKubernetesAzure DevOpsModel Context Protocol

Soft Skills

leadershipcommunicationproblem-solvingcollaborationorganizational change