Staff AI Solutions Engineer

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 (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 workflows.

  • 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.

Bonus Points:

• Prior experience working in high-growth, pre-IPO companies and/or industry experience in Technology or Healthcare preferred.

• Affinity for bad jokes.

Physical/Cognitive Requirements: