Applied AI Engineer
The Applied AI Engineer role at Amigo is a launchpad. You'll ship production AI agents end-to-end — writing the code, shaping product decisions, and sitting with customers as they deploy — and in doing so, you'll build the exact skill set that opens doors to the next chapter of your career at Amigo. This role is designed to help you grow into other parts of the company. Applied AI Engineers move into one of four paths within 6–12 months:
Applied AI: stay in the org; go deeper on agent architecture, verification frameworks, and the hardest customer problems; become a technical leader on the applied team
Product Management: if you gravitate toward roadmap, discovery, and shaping what we build next
Sales Engineering: if you thrive in pre-sales, technical storytelling, and closing enterprise deals
Software Engineering: if you want to go deeper on platform, infrastructure, and core systems
You won't have to guess which fits. This role will actively help you explore each path, give you projects that stretch in those directions, and sponsor the internal transition when you're ready. People who want to stay on the applied side long-term can do that too — but this role is explicitly built so you don't have to.
What You'll Do
Implement context graphs that enable agents to select the right reasoning mode (lookup, pattern recognition, exploration) based on problem complexity
Build and optimize agent configurations, including static personas, dynamic behaviors, and functional memory systems
Design bounded Operational Patient Domains (OPDs) with explicit inclusions, exclusions, capabilities, and escalation rules
Implement verification loops and simulation-based testing to validate agent performance against customer KPIs
Write Python code for tool integrations and API orchestration within customer environments
Build evaluation suites that gate deployments using customer-specific success metrics
Collaborate with domain experts through structured interviews to capture reasoning patterns and clinical protocols
Support technical power users through git-based workflows for rapid iteration
Implement adversarial testing to systematically identify and prevent failure modes
Ensure agents maintain audit-ready provenance with version pinning and evidence links
Contribute to systematizing implementation patterns across different problem domains
What We're Looking For
2+ years of production software engineering experience
Strong Python skills including proper use of typing, and experience building highly reliable systems that interact with external APIs (schema design, retry strategies, error propagation)
Experience with LLMs, prompt engineering, and building on AI platforms
Ability to implement systems with strict reliability requirements
Understanding of testing and verification methodologies
Experience working directly with customers or stakeholders to deliver technical solutions
Familiarity with git workflows and collaborative development
Strong debugging and problem-solving skills for complex systems
Ability to balance theoretical concepts with practical implementation constraints
Clear technical communication for both engineering and non-technical audiences
Nice To Have
Experience in regulated industries (healthcare, finance, legal)
Background with simulation, synthetic data, or evaluation frameworks
Understanding of distributed systems and service-oriented architectures
Experience with observability and monitoring in production environments
Familiarity with compliance requirements (HIPAA, SOC 2, GDPR)
Benefits
Health & Wellness
Comprehensive health, dental, and vision insurance
Daily catered lunch and dinner
Mental health support and wellness coaching
Flexible wellness stipend for fitness, therapy, or personal growth
Growth & Development
Annual learning budget for courses, books, or conferences
Conference attendance budget for professional development
Annual team offsite
Academic collaboration opportunities
Unlimited PTO
Our Core Values
Patients Win, We Win
If patients aren't getting better care, we haven't earned the right to scale. Every internal decision gets pressure-tested: does this make patients' lives better? If we can't draw the line, we question why we're doing it.
High Standards, High Care
We hold a high bar for the team because patients are counting on us to get this right. But high standards only work with genuine investment in each other. You can take risks, admit mistakes, and challenge ideas—not despite our standards, but because of them.
Thoughtful Urgency
We move fast by default, but speed without judgment is recklessness. The discipline is knowing which decisions are reversible vs. not. In healthcare AI, the companies that win will be fast everywhere they can be and careful everywhere they must be. We build the muscle to do both.
Intensely Measured
We instrument patient outcomes, provider ROI, system performance, and clinical accuracy. But data without action is surveillance. Every metric should have an owner, a threshold, and a response plan. If we're measuring something but never acting on it, we stop measuring it.
Who Builds With Us
Low ego:
Politics and territory don't interest you. The best ideas win, regardless of who has them.
Direct:
You say the hard thing, challenge ideas openly, and commit fully once decided.
High agency:
You thrive on trust rather than instruction. When you see something is broken, you fix it. You don't file tickets and wait for someone else.
Bar of excellence:
You hold yourself to a bar most people wouldn't, and you want teammates who do the same.
Skeptical:
You push back on rules that don't make sense and question assumptions that haven't earned their place.