Senior AI Engineer, Agentic Healthcare Platform - Remote

Baylor Scott & White Health; Dallas (TX); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

Health benefits

401k match

Tuition reimbursement

PTO from day 1

Job summary

Baylor Scott & White Health seeks a senior backend engineer to build a production-grade GenAI platform for patient-facing and enterprise experiences. You will design and operate backend AI services, agentic workflows, and secure clinical integrations using Python, FastAPI, and MCP tool servers.

You will own end-to-end delivery, enforce PHI protection, and collaborate with product, security, architecture, and clinical teams to ship safe AI capabilities at scale.

Qualifications

• 7+ years of professional software development experience.
• Strong production experience with Python, asynchronous programming, backend APIs, and service-oriented architecture.
• Hands-on experience building GenAI applications with Google ADK 2.x or similar agent orchestration frameworks.
• Demonstrated understanding of LLM tool calling, multi-agent workflows, structured outputs, RAG, prompt engineering, and AI safety/guardrail patterns.
• Experience integrating with LLM providers such as Google Gemini/Vertex AI, OpenAI, Anthropic, or equivalent platforms.
• Experience with FastAPI, pytest, Docker, CI/CD workflows, and containerized production deployments.
• Strong understanding of API security, JWT/OAuth, delegated authorization, secrets management, and secure service-to-service communication.
• Ability to design safe integration patterns for healthcare data, clinical systems, or other sensitive regulated environments.
• Experience with cloud-native services in Azure and/or Google Cloud.
• Strong debugging, testing, and operational problem-solving skills.
• Ability to work with high ownership in a small, fast-moving engineering team.
• Strong communication skills and ability to collaborate across engineering, architecture, security, product, and clinical stakeholders.

Responsibilities

• Design, implement, and maintain backend AI services using Python 3.12+, async service patterns, and frameworks such as FastAPI, Google ADK, and FastMCP.
• Develop and operate agentic GenAI workflows using Google ADK 2.x as a primary orchestration framework.
• Build MCP tool servers and domain-specific tools for healthcare workflows such as care tasks, medications, scheduling, patient profile lookup, clinical record retrieval, and EHR/FHIR-backed experiences.
• Integrate LLM providers and GenAI toolkits including Google Gemini/Vertex AI, OpenAI, Anthropic, and related orchestration frameworks where appropriate.
• Implement prompt engineering, RAG, structured output validation, tool selection patterns, memory strategies, and AI guardrails to support safe and reliable model behavior.
• Design secure clinical integration boundaries for FHIR, EHR, user profile, scheduling, and other healthcare systems.
• Protect PHI and sensitive credentials through secure JWT/OAuth flows, delegated authorization, token forwarding, encryption, redaction, and audit-safe logging.
• Leverage cloud services such as Azure App Configuration, Key Vault, Cosmos DB, Event Hubs, Application Insights, and Google Cloud services such as Vertex AI/Gemini and Cloud Run.
• Build observability, telemetry, evaluation, and impact-reporting capabilities for AI systems and AI-assisted engineering workflows.
• Author and maintain automated tests using pytest and support linting, type-checking, CI/CD, and containerized deployments.
• Collaborate with product, architecture, security, clinical, platform, DevOps, and application teams to deliver safe AI capabilities through well-defined HTTP/MCP contracts.
• Participate in architecture discussions, code reviews, production support, operational readiness, and incident response.

Skills

Python

GenAI

Backend APIs

FastAPI

OAuth/JWT

Tools

FastAPI

pytest

Docker

CI/CD

Azure

Vertex AI

Job description

Role Summary

We are seeking a senior AI engineer to help build and operate a production-grade healthcare GenAI platform for patient-facing and enterprise conversational experiences. This role will focus on backend AI services, agentic workflows, MCP tool servers, secure clinical data integrations, and AI platform tooling.

The ideal candidate is a strong Python/backend engineer with hands-on GenAI experience who can build reliable agent systems using Google ADK, LLM tool calling, structured outputs, RAG, guardrails, and healthcare-safe integration patterns. This is not a prompt-only role; it requires production engineering judgment across security, observability, testing, deployment, PHI protection, and operational ownership.

Primary Responsibilities

• Design, implement, and maintain backend AI services using Python 3.12+, async service patterns, and frameworks such as FastAPI, Google ADK, and FastMCP.
• Develop and operate agentic GenAI workflows using Google ADK 2.x as a primary orchestration framework.
• Build MCP tool servers and domain-specific tools for healthcare workflows such as care tasks, medications, scheduling, patient profile lookup, clinical record retrieval, and EHR/FHIR-backed experiences.
• Integrate LLM providers and GenAI toolkits including Google Gemini/Vertex AI, OpenAI, Anthropic, and related orchestration frameworks where appropriate.
• Implement prompt engineering, RAG, structured output validation, tool selection patterns, memory strategies, and AI guardrails to support safe and reliable model behavior.
• Design secure clinical integration boundaries for FHIR, EHR, user profile, scheduling, and other healthcare systems.
• Protect PHI and sensitive credentials through secure JWT/OAuth flows, delegated authorization, token forwarding, encryption, redaction, and audit-safe logging.
• Leverage cloud services such as Azure App Configuration, Key Vault, Cosmos DB, Event Hubs, Application Insights, and Google Cloud services such as Vertex AI/Gemini and Cloud Run.
• Build observability, telemetry, evaluation, and impact-reporting capabilities for AI systems and AI-assisted engineering workflows.
• Author and maintain automated tests using pytest and support linting, type-checking, CI/CD, and containerized deployments.
• Collaborate with product, architecture, security, clinical, platform, DevOps, and application teams to deliver safe AI capabilities through well-defined HTTP/MCP contracts.
• Participate in architecture discussions, code reviews, production support, operational readiness, and incident response.

Benefits

• Immediate eligibility for health and welfare benefits
• 401(k) savings plan with dollar-for-dollar match up to 5%
• Tuition Reimbursement
• PTO accrual beginning Day 1

Note: Benefits may vary based upon position type and/or level

Required Qualifications

• 7+ years of professional software development experience.
• Strong production experience with Python, asynchronous programming, backend APIs, and service-oriented architecture.
• Hands-on experience building GenAI applications with Google ADK 2.x or similar agent orchestration frameworks.
• Demonstrated understanding of LLM tool calling, multi-agent workflows, structured outputs, RAG, prompt engineering, and AI safety/guardrail patterns.
• Experience integrating with LLM providers such as Google Gemini/Vertex AI, OpenAI, Anthropic, or equivalent platforms.
• Experience with FastAPI, pytest, Docker, CI/CD workflows, and containerized production deployments.
• Strong understanding of API security, JWT/OAuth, delegated authorization, secrets management, and secure service-to-service communication.
• Ability to design safe integration patterns for healthcare data, clinical systems, or other sensitive regulated environments.
• Experience with cloud-native services in Azure and/or Google Cloud.
• Strong debugging, testing, and operational problem-solving skills.
• Ability to work with high ownership in a small, fast-moving engineering team.
• Strong communication skills and ability to collaborate across engineering, architecture, security, product, and clinical stakeholders.

Preferred Qualifications

• Healthcare domain experience, especially with HIPAA, HL7/FHIR, Epic integrations, patient portals, scheduling, medication, claims, or clinical workflow systems.
• Experience with MCP, FastMCP v3, model-tool integration, or agent tool-server architecture.
• Experience with LangGraph, LangChain, PydanticAI, Semantic Kernel, or other orchestration and structured-output frameworks.
• Familiarity with OpenTelemetry, Application Insights, Kusto, AI evaluation pipelines, or production observability tooling.
• Experience building AI platform tooling, internal developer enablement tools, AI governance automation, code review automation, or AI impact reporting.
• Experience with uv, ruff, pyright, Python packaging, dependency isolation, and multi-container service deployment.
• Exposure to React and TypeScript for occasional full-stack collaboration, while remaining primarily backend/platform focused.

AI-Augmented Development Expectations

This role requires disciplined use of AI tools as part of daily engineering work. The engineer should be able to use AI coding agents and GenAI systems to accelerate implementation, research, validation, and documentation while remaining fully accountable for correctness and safety.

The Successful Candidate Will Know How To

• Give AI agents clear, scoped engineering tasks.
• Require agents to inspect existing code, contracts, tests, and architecture before proposing changes.
• Validate AI-generated code with tests, type checks, linting, runtime review, and security judgment.
• Identify when generated code is unsafe, incorrect, over-engineered, noncompliant, or inconsistent with platform boundaries.
• Use AI tools to improve delivery speed without compromising PHI protection, reliability, auditability, maintainability, or user trust.
• Contribute to AI governance patterns, reusable agent instructions, tool ergonomics, and platform standards.

Ideal Candidate Profile

The ideal candidate is a senior backend/platform engineer with practical GenAI experience and strong healthcare-grade judgment. They can design agent workflows, but they also understand authentication, deployment, observability, testing, failure modes, PHI handling, and operational ownership.

They are pragmatic, security-minded, collaborative, and comfortable building early platform foundations that will later support production patient-facing AI experiences.