AI Engineer (Remote)

M3USA•Fort Washington, PA

1d•Remote

About The Position

The AI Engineer will build AI-powered systems that automate and improve workflows for M3 USA’s healthcare businesses. The primary customers are non-technical business teams. You will work closely with them to understand their processes and pain points, then design and implement solutions that make them more effective. This role is centered on agentic coding: using AI coding agents (Claude Code, OpenAI Codex, OpenCode, or similar) to build software an order of magnitude more rapidly than traditional methods. You should be an expert practitioner who has shipped real applications this way, not just experimented with the tools. You should have solid engineering fundamentals and the ability to critically review and improve on AI-generated plans and thoughtfully consider technology frameworks and architectures to facilitate effective AI-enabled development. You can flexibly adapt to new tools and apply them creatively to create new products and services and solve business problems. As a Central Services position, you will work across M3 USA subsidiaries including Wake Research, PracticeMatch, The Medicus Firm, and others. This position will report to the CTO (Brian Hooper).

Requirements

Bachelor's degree in computer science or related field OR equivalent work experience
Demonstrated agentic coding expertise: Extensive hands-on experience using Claude Code, OpenAI Codex, OpenCode, or similar AI coding agents. You should have built multiple applications this way and be able to discuss specific techniques, workflows, and tradeoffs.
Portfolio of AI-developed projects: Tangible examples of applications you’ve built using agentic coding. We want to see GitHub repos, deployed demos, or production systems that show your ability to ship with AI assistance.
Production deployment experience: You have deployed AI-powered applications that are used by real end users (not just prototypes). You understand the operational challenges of maintaining AI systems in production.
Workflow automation experience: Track record of automating manual processes, whether through custom scripts, integrations, or low-code platforms.
Technical foundations: Python, TypeScript, web application development, SQL and databases, LLM APIs (Claude, OpenAI), prompt engineering, context engineering, REST APIs. Experience with cloud infrastructure (AWS, GCP, or Azure). Comfortable learning new languages and frameworks quickly with AI assistance.
AI fundamentals: Understanding of underlying AI technologies including transformers, neural networks, agentic loops, and context, among other topics. You should be able to reason about model capabilities and limitations based on architectural understanding.
Agentic systems experience: Hands-on experience building agentic workflows, multi-agent systems, or AI assistants with tool use. Understanding of when to use agentic patterns versus simpler approaches.

Responsibilities

Agentic Development & Automation:
Use AI coding agents (Claude Code, OpenAI Codex, OpenCode, or similar) to rapidly build and iterate on production applications. You should be comfortable building full-stack applications in days or hours, not weeks.
Automate business workflows and processes by building custom tools, scripts, and integrations that reduce manual work.
Stay current with the latest agentic coding techniques, tools, and best practices. Evaluate emerging capabilities and share informed opinions on what works in production.
AI Solutions Development:
Develop LLM-based applications including document processing, intelligent assistants, and data extraction workflows.
Create proof-of-concepts to demonstrate feasibility and business value of AI initiatives.
Deploy AI applications to production and support real end users. Monitor usage, gather feedback, and iterate.
Requirements & Stakeholder Communication:
Translate business needs into technical requirements by observing how people actually work, not just what they ask for.
Communicate progress and tradeoffs clearly to non-technical stakeholders.
Operations & Governance:
Ensure AI solutions meet security, privacy, and compliance requirements, which is particularly important given healthcare data.
Monitor and maintain deployed solutions; iterate based on user feedback.
Manage costs and provide input on AI tools and platform selection.