AI Platform / Software Engineer (Agentic Workflow)

Summary
We are seeking a hands-on AI Solutions Engineer to build the technical foundation that enables non-technical users and internal teams to create functional, production-ready systems with AI. In this role, you will not just build applications; you will create the scaffolding, guardrails, reusable components, and delivery standards that allow AI-assisted development to scale across the organization. For complex or higher-risk solutions, you will step in directly to design, harden, and productionize the implementation.

Responsibilities

• Design Agentic Workflows: Build workflows that translate natural-language business requirements into structured specifications, application logic, integrations, testing, and deployable solutions

• Build Reusable Capabilities: Create reusable skills, prompts, templates, connectors, APIs, and technical components that allow non-technical users to build solutions with coding agents.

• Establish Guardrails & Governance: Implement automated testing, validation, security controls, permissions, audit logging, and risk-based human review for AI-generated systems

• Solution Architecture: Define practical architecture patterns for APIs, data flows, integrations, authentication, agent tool usage, and deployment.

• Integrate & Deploy: Connect AI-assisted solutions with enterprise systems, databases, authentication, cloud services, and production environments.

• Production Hardening: Step in on complex or higher-risk solutions to review, refactor, troubleshoot, and harden AI-generated applications before production.

Required Technical Skills

• AI & Agentic Engineering: Practical experience with coding agents such as Codex, Claude Code, Cursor, or similar tools, plus agent frameworks or SDKs such as LangGraph, OpenAI Agents SDK, CrewAI, or equivalent.

• Software Engineering: Strong proficiency in JavaScript/TypeScript, Python, APIs, databases, and system integration.

• System Architecture: Strong understanding of cloud platforms (AWS, Azure, or GCP), authentication, security, application architecture, and CI/CD.

• Production Engineering: Experience with automated testing, observability, secrets management, permissions, reliability, and production troubleshooting.

• LLM Engineering: Strong understanding of prompting, tool calling, structured outputs, RAG, evaluation, context management, and human-in-the-loop workflows.

Soft Skills & Qualities

• Systemic Thinking: Focus on building the factory that makes software, rather than repeatedly building individual software pieces.

• Technical Judgment: Balance delivery speed with security, reliability, maintainability, and cost.

• Empathy for End Users: Understand both technical and non-technical user needs and turn complex engineering capabilities into simple, usable workflows.

• Rapid Prototyping: A bias for action, experimentation, and iterative delivery.