AI Platform Engineer

AI Platform Engineer

Location: Remote

Type: Full-Time

Role Overview

A fast-growing SaaS company serving healthcare practices across the U.S. We are seeking a highly hands-on Lead AI Platform Engineer to design and build a centralized AI platform embedded directly into our core product. 

This platform will power intelligent, context-aware workflows across our application, enabling recommendations, automation, and decision support within a highly regulated HR environment. We've already validated AI use cases within our product and are now investing in building a scalable, production-grade foundation.

This is a 70–80% hands-on role. You will be the primary architect and builder responsible for turning. AI capabilities into reliable, production-grade systems that operate safely, respect permissions, and deliver real value to users.

You will report directly to the CTO and play a foundational role in shaping how AI is implemented across the company, with the opportunity to build and lead a team over time. You will have significant autonomy in defining architecture, tooling, and implementation approach.

This is not a research or prompt experimentation role; this is about building reliable systems that operate in production.

You will design and implement systems that:

• Generate context-aware recommendations based on structured application data and internal knowledge (including HR best practices and legal guidance)
• Embed AI directly into core product workflows, not as standalone tools
• Support multi-step workflows, including planning, tool use, validation, and controlled execution
• Enforce strict guardrails and validation layers to ensure outputs are accurate, compliant, and within scope
• Respect user roles and permissions, including enabling AI to take actions on behalf of users when appropriate
• Manage state, memory, and error recovery in production environments
• Example Problems You Might Work On
• Suggesting compliant HR policy language based on company-specific context and internal knowledge bases
• Recommending employee actions or documentation with validation against internal rules, permissions, and regulatory constraints
• Enabling AI-assisted workflows that can take action on behalf of users within clearly defined and auditable boundaries

Key Responsibilities

Build the AI Platform

• Architect and implement the core AI platform within a Laravel + AWS environment
• Define patterns for prompt orchestration, state management, and workflow execution
• Integrate structured application data and internal knowledge sources into AI workflows
• Define evaluation and feedback loops to continuously improve AI output quality and reliability

Ship Product-Facing AI Features

• Deliver AI-powered consultative features embedded in core user workflows
• Enable users to receive high-quality, context-driven recommendations and automation

Design for Safety and Compliance

• Build robust guardrails, validation layers, and monitoring systems
• Ensure all AI outputs and actions are reliable and aligned with regulatory expectations in a sensitive HR environment

Enable the Engineering Team

• Create abstractions and frameworks that allow other engineers to safely build on the AI platform
• Act as the internal technical leader for applied AI implementation

Own the System Long-Term

• Ensure the platform is scalable, maintainable, and cost-effective
• Shape the roadmap for AI capabilities across the organization
• Participate in hiring and building the AI team over time

What Success Looks Like (First 6 Months)

• Design and launch the foundational AI platform architecture within our existing stack
• Ship AI-powered features into production that enhance core product workflows
• Establish guardrails, validation mechanisms, and permission-aware execution
• Define reusable patterns that enable other engineers to contribute safely
• Lay the groundwork for scaling AI capabilities across the product and team

Technical Requirements

Strong Backend Engineering Foundation

• Deep experience with PHP and Laravel (service container, queues, architecture patterns)
• Proven ability to design and maintain production-grade systems

Applied AI Experience

• Experience integrating LLMs into real-world, production applications
• Strong understanding of building multi-step AI workflows (planning, execution, validation, tool use)
• Experience managing context, memory, and reliability in AI systems

Infrastructure Experience

• Meaningful hands-on experience designing and operating production systems on AWS
• Familiarity with services such as Lambda, RDS, SQS, and event-driven architecture patterns
• Comfort with distributed systems and asynchronous processing

System Design & Ownership

• Ability to architect systems end-to-end with a focus on reliability and maintainability
• Strong judgment on tradeoffs between speed, cost, and quality

Preferred Experience

• Experience embedding AI into SaaS product workflows (not just internal tools)
• Familiarity with permissioned systems and role-based access control
• Experience integrating LLMs into Laravel-based applications is strongly preferred; experience with the Laravel AI SDK is a plus
• Experience leading technical initiatives or mentoring engineers

Who You Are

• A builder first: you ship working systems, not just ideas
• Energized by greenfield work: you're at your best when defining the architecture
• Product-minded: you care about delivering real user value, not just technical novelty
• Pragmatic: you understand the limitations of AI and design systems accordingly
• Ownership-driven: you take responsibility for outcomes, not just code
• Comfortable operating with ambiguity and defining the path forward