Full-Stack Engineer (Front-End Leaning)

About the Role

We are seeking a forward-thinking engineer to lead our internal AI transformation initiative. This role sits at the intersection of infrastructure, machine learning, and organizational change. You will architect and deploy AI systems that reshape how our own teams build, operate, and collaborate—making the company itself a showcase for intelligent automation.

This is not a research position. You will ship production systems that touch every function: engineering, product, finance, operations, and customer success. The work you do here becomes the template we share with the world.

What You Will Do

• Architect Internal AI Platforms
Design and build the infrastructure that enables AI agents, natural language interfaces, and predictive systems to operate securely across our internal tools and data stores.

• Automate Operational Workflows
Identify high-friction processes within the company—reporting, triage, routing, documentation—and replace them with intelligent, autonomous systems.

• Integrate AI into Engineering Pipelines
Embed AI assistance into code review, testing, deployment, monitoring, and incident response. Reduce toil without reducing human oversight.

• Enable Self-Service Intelligence
Build interfaces that let non-technical employees query data, generate reports, and receive insights without engineering support.

• Drive Organizational Adoption
Partner with department heads to understand pain points, prototype solutions, and roll out AI tools with training and feedback loops built in.

• Establish Governance and Guardrails
Define policies for data access, model behavior, output validation, and human-in-the-loop requirements. Ensure compliance without killing velocity.

• Measure and Communicate Impact
Track time saved, error rates reduced, and decision speed improved. Present findings to leadership and iterate based on real usage data.

What We Are Looking For

Required Experience

• 5+ years in software engineering with a focus on backend systems, infrastructure, or platform engineering
• 2+ years working directly with machine learning systems in production environments
• Deep familiarity with LLMs, embedding models, retrieval-augmented generation, and agent architectures
• Proven track record of building internal tools or platforms adopted by cross-functional teams
• Strong proficiency in Python and at least one systems language (Go, Rust, or Java)
• Experience with cloud-native infrastructure (AWS, GCP, or Azure) and container orchestration
• Understanding of data pipelines, vector databases, and real-time inference serving

Preferred Experience

• Previous role in an AI-native company or a significant internal AI transformation program
• Background in developer productivity, platform engineering, or SRE with an automation focus
• Experience with fine-tuning, prompt engineering, or model evaluation methodologies
• Familiarity with enterprise security requirements: SSO, SCIM, audit logging, data residency
• Contributions to open-source ML infrastructure projects

Core Competencies

• First-Principles Thinking
You deconstruct problems to their essence and rebuild solutions from fundamentals rather than copying patterns.

• Bias for Action
You prototype quickly, validate with real users, and iterate rather than over-planning.

• Systems Orientation
You see how engineering decisions ripple across teams, tools, and timelines. You optimize for the whole, not the part.\

• Communication Clarity
You translate technical complexity into language that executives, operators, and engineers all understand.

• Change Leadership
You do not just build systems. You bring people along, addressing skepticism and building trust in AI as a teammate.