Included Health is seeking a hands‑on Staff AI Solutions Engineer to be part of our IT Solutions team. The right candidate will be passionate about the advancements in AI and will have experience in owning deployment/hosting decisions and implementation.

The engineer in this role will design, build, and operate internal automations, AI agents, and secure integrations that increase corporate teams productivity while meeting healthcare security and compliance requirements. This role blends software engineering, systems integration, architecture, and practical LLM expertise to take POCs to production.

The engineer in this role will partner closely with and build internal solutions for Cybersecurity, Compliance, Finance, HR, Product, Engineering, Operations, Clinical teams, and business stakeholders to deliver AI tooling with metrics-backed value across the enterprise.

This role will report to the Director, Digital Workplace

Responsibilities:

Design, build, deploy, and maintain production LLM‑based solutions and agent workflows.
Own the technical strategy and reference architecture for enterprise AI solutions across multiple teams and business functions.
Lead high-complexity, cross-functional AI initiatives from ambiguous problem definition through production adoption and measurable business outcomes.
Define and evolve reusable platform capabilities, implementation standards, and governance patterns that enable safe, scalable AI adoption beyond a single team.
Review citizen developer AI agents / solutions to provide recommendations for optimization, ensure compliance with guidelines and measure value.
Influence roadmap and investment decisions across the company through technical leadership and business-value analysis.
Drive technical debates, align stakeholders on tradeoffs, and unblock multi-team execution for strategically important AI initiatives.
Implement, review, and validate code produced by models; write production‑quality code and run code reviews to ensure correctness and security.
Build robust integrations and connectors (MCP, REST/GraphQL APIs, webhooks, SDKs, CLIs) between AI tooling and enterprise SaaS (e.g., Okta, Google Workspace, Slack, Jira, Confluence, Jamf).
Own end‑to‑end deployment and lifecycle for AI services: CI/CD pipelines, Infrastructure as Code modules (Terraform), cloud deployment (GCP/AWS), monitoring, and incident/runbook playbooks.
Establish and operate model evaluation, monitoring, and governance: accuracy and safety metrics, hallucination detection, drift monitoring, telemetry, alerting, and human‑in‑the‑loop controls.
Lead vendor evaluations and POCs across commercial and open‑source LLM/agent platforms; produce comparative performance, risk, and TCO recommendations to inform adoption.
Partner with Cybersecurity and Compliance to design PHI‑safe data handling patterns (sanitization, tokenization, least‑privilege access, audit logging) and ensure AI solutions align with relevant controls and policies.
Create and maintain architecture diagrams, API documentation, runbooks, support documentation, and onboarding materials so solutions are maintainable and auditable.
Mentor engineers and influence architectural standards for AI/LLM adoption across Digital Workplace; contribute reusable libraries and IaC modules to accelerate future builds.
Drive automation of operational tasks (provisioning, onboarding, common workflows) via agents and workflow tooling to reduce manual processes.
Partner with Technology Services leadership to implement AI spend management tools and value tracking.

Qualifications:

Education & Experience:
- Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
- 8+ years professional software engineering / systems integration experience.
- Technical Skills:
  - Practical experience owning the complete lifecycle of LLMs and agent
  - Experience evaluating model performance, mitigating hallucinations and bias, and implementing human‑in‑the‑loop controls.
  - Ability to define reference architectures and reusable patterns for AI services used across multiple teams.
  - Experience making architectural tradeoffs across reliability, latency, cost, security, and maintainability in production systems.
  - Experience establishing engineering standards, guardrails, and paved-road patterns for AI development and deployment.
  - Experience optimizing model/runtime cost, usage controls, and value measurement.
  - Strong coding experience in Python and/or TypeScript/JavaScript with production software engineering discipline (code reviews, testing, CI/CD).
  - Experience designing and building API integrations (REST/GraphQL), webhooks, and custom connectors to SaaS applications.
  - Experience with Infrastructure as Code (Terraform) and deploying services to cloud platforms (GCP/AWS).
  - Familiarity with CI/CD tooling and observability best practices (metrics, logs, tracing).
  - Working knowledge of security best practices for data‑sensitive systems and experience collaborating with Security & Compliance teams. Experience in healthcare or other regulated environments is strongly preferred.
  - Soft Skills:
    - Strong communicator and collaborator who can translate ambiguous business needs into technical designs and influence cross‑functional stakeholders.
    - Bias for action: ability to move quickly from POC to production while keeping operational rigor with change management.
    - Detail oriented with a security‑first mindset.
    - Proven ability to influence technical direction and align cross-functional stakeholders without direct authority.
    - Skilled at leading technical debates, resolving conflict, and driving decisions in ambiguous, high-stakes environments.
    - Strong executive communication skills, with the ability to translate complex technical concepts into clear business decisions and risk tradeoffs.

Physical/Cognitive Requirements:

Physical Requirements:

Stamina, strength, mobility, manual dexterity, vision, hearing, environmental factors.

Ability to effectively communicate and engage with others, with accommodations available for visual or auditory impairments

Ability to handle physical tasks related to the job, with accommodations provided as needed.

Ability to respond effectively to changing situations, with accommodations available for varying response times.

Prompt and regular attendance at assigned work location.

Ability to remain seated in a stationary position for prolonged periods.

Requires eye-hand coordination and manual dexterity sufficient to operate keyboard, computer and other office-related equipment.

No heavy lifting is expected, though occasional exertion of about 20 lbs. of force (e.g., lifting a computer / laptop) may be required.

Cognitive Requirements:

Problem-solving, decision-making, attention to detail, critical thinking, communication skills, stress tolerance.

Facilitate communication and collaboration with clients and stakeholders.

Adaptability to different work environments, with support provided for managing workload and pace

Ability to maintain focus and attention on tasks, with accommodations available for individuals with attention-related conditions.

Ability to interact with leadership, employees, and members in an appropriate manner.

This role will report to the Director, Digital Workplace

Responsibilities:

Design, build, deploy, and maintain production LLM‑based solutions and agent workflows.
Own the technical strategy and reference architecture for enterprise AI solutions across multiple teams and business functions.
Lead high-complexity, cross-functional AI initiatives from ambiguous problem definition through production adoption and measurable business outcomes.
Define and evolve reusable platform capabilities, implementation standards, and governance patterns that enable safe, scalable AI adoption beyond a single team.
Review citizen developer AI agents / solutions to provide recommendations for optimization, ensure compliance with guidelines and measure value.
Influence roadmap and investment decisions across the company through technical leadership and business-value analysis.
Drive technical debates, align stakeholders on tradeoffs, and unblock multi-team execution for strategically important AI initiatives.
Implement, review, and validate code produced by models; write production‑quality code and run code reviews to ensure correctness and security.
Build robust integrations and connectors (MCP, REST/GraphQL APIs, webhooks, SDKs, CLIs) between AI tooling and enterprise SaaS (e.g., Okta, Google Workspace, Slack, Jira, Confluence, Jamf).
Own end‑to‑end deployment and lifecycle for AI services: CI/CD pipelines, Infrastructure as Code modules (Terraform), cloud deployment (GCP/AWS), monitoring, and incident/runbook playbooks.
Establish and operate model evaluation, monitoring, and governance: accuracy and safety metrics, hallucination detection, drift monitoring, telemetry, alerting, and human‑in‑the‑loop controls.
Lead vendor evaluations and POCs across commercial and open‑source LLM/agent platforms; produce comparative performance, risk, and TCO recommendations to inform adoption.
Partner with Cybersecurity and Compliance to design PHI‑safe data handling patterns (sanitization, tokenization, least‑privilege access, audit logging) and ensure AI solutions align with relevant controls and policies.
Create and maintain architecture diagrams, API documentation, runbooks, support documentation, and onboarding materials so solutions are maintainable and auditable.
Mentor engineers and influence architectural standards for AI/LLM adoption across Digital Workplace; contribute reusable libraries and IaC modules to accelerate future builds.
Drive automation of operational tasks (provisioning, onboarding, common workflows) via agents and workflow tooling to reduce manual processes.
Partner with Technology Services leadership to implement AI spend management tools and value tracking.

Qualifications:

Education & Experience:
- Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
- 8+ years professional software engineering / systems integration experience.
- Technical Skills:
  - Practical experience owning the complete lifecycle of LLMs and agent
  - Experience evaluating model performance, mitigating hallucinations and bias, and implementing human‑in‑the‑loop controls.
  - Ability to define reference architectures and reusable patterns for AI services used across multiple teams.
  - Experience making architectural tradeoffs across reliability, latency, cost, security, and maintainability in production systems.
  - Experience establishing engineering standards, guardrails, and paved-road patterns for AI development and deployment.
  - Experience optimizing model/runtime cost, usage controls, and value measurement.
  - Strong coding experience in Python and/or TypeScript/JavaScript with production software engineering discipline (code reviews, testing, CI/CD).
  - Experience designing and building API integrations (REST/GraphQL), webhooks, and custom connectors to SaaS applications.
  - Experience with Infrastructure as Code (Terraform) and deploying services to cloud platforms (GCP/AWS).
  - Familiarity with CI/CD tooling and observability best practices (metrics, logs, tracing).
  - Working knowledge of security best practices for data‑sensitive systems and experience collaborating with Security & Compliance teams. Experience in healthcare or other regulated environments is strongly preferred.
  - Soft Skills:
    - Strong communicator and collaborator who can translate ambiguous business needs into technical designs and influence cross‑functional stakeholders.
    - Bias for action: ability to move quickly from POC to production while keeping operational rigor with change management.
    - Detail oriented with a security‑first mindset.
    - Proven ability to influence technical direction and align cross-functional stakeholders without direct authority.
    - Skilled at leading technical debates, resolving conflict, and driving decisions in ambiguous, high-stakes environments.
    - Strong executive communication skills, with the ability to translate complex technical concepts into clear business decisions and risk tradeoffs.

Physical/Cognitive Requirements:

Physical Requirements:

Stamina, strength, mobility, manual dexterity, vision, hearing, environmental factors.

Ability to effectively communicate and engage with others, with accommodations available for visual or auditory impairments

Ability to handle physical tasks related to the job, with accommodations provided as needed.

Ability to respond effectively to changing situations, with accommodations available for varying response times.

Prompt and regular attendance at assigned work location.

Ability to remain seated in a stationary position for prolonged periods.

Requires eye-hand coordination and manual dexterity sufficient to operate keyboard, computer and other office-related equipment.

No heavy lifting is expected, though occasional exertion of about 20 lbs. of force (e.g., lifting a computer / laptop) may be required.

Cognitive Requirements:

Problem-solving, decision-making, attention to detail, critical thinking, communication skills, stress tolerance.

Facilitate communication and collaboration with clients and stakeholders.

Adaptability to different work environments, with support provided for managing workload and pace

Ability to maintain focus and attention on tasks, with accommodations available for individuals with attention-related conditions.

Ability to interact with leadership, employees, and members in an appropriate manner.

Staff AI Solutions Engineer

Responsibilities:

Qualifications:

Physical/Cognitive Requirements:

Related jobs

Staff AI Solutions Engineer

Responsibilities:

Qualifications:

Physical/Cognitive Requirements:

Related jobs