About This Role
This hands-on engineering role involves winning technical deals by demonstrating to enterprise customers that their AI agents will function effectively at scale and under their specific rules. The Solutions Architect will build demos and proofs of concept, address architectural questions, and then deliver an executable plan. The role involves writing code most weeks and engaging with CIOs, platform teams, and risk officers.
Responsibilities
- Run technical discovery with CIOs, platform-engineering, and risk teams at banks, insurers, and asset managers
- Map customer systems, data, and controls to relevant agent use cases
- Build tailored demos of agent workflows and the AI Control Plane, using customer scenarios where possible
- Scope and deliver 2–6 week proofs of concept, including writing code, wiring integrations, and measuring results against agreed success criteria
- Produce reference architectures for agent deployment on AWS, Azure, or GCP, covering identity, data access, observability, and guardrails
- Answer security, compliance, and model-risk questionnaires (SOC 2, data residency, audit logging, human-in-the-loop controls)
- Convert every successful POC into a delivery plan for forward-deployed engineers
- Bring field patterns to product development
- Build reusable demo assets, templates, and starter agents for the team
Requirements
- 8+ years in software engineering, solutions engineering, solutions architecture, or forward-deployed engineering for enterprise customers
- Hands-on experience building LLM applications or agents: prompt and tool design, RAG, evaluation, and at least one framework such as Lang Graph, CrewAI, Semantic Kernel, or the OpenAI or Anthropic agent SDKs
- Strong Python, plus working knowledge of APIs, containers, and one major cloud
- Ability to explain an architecture to an engineer and its business case to a CIO in the same meeting
- Comfort in regulated environments, where governance, auditability, and access control are critical
- Consulting experience: client-facing work at a consulting firm or systems integrator, scoping engagements, running workshops, and owning outcomes with enterprise stakeholders
- Solid understanding of agentic tools, AI governance, and agent deployment: tool calling and MCP, guardrails and policy controls, access management, observability and audit trails, and taking agents from pilot to production at enterprise scale
Nice to have:
- Experience selling to, or working inside, financial services
- Kubernetes, observability, or AIOps tooling
- Cloud or AI certifications (AWS, Azure, GCP)
- A public demo, repo, talk, or post about something built with agents
Benefits
- Early-employee equity
- Employer-paid health insurance
- Budget for certifications, conferences, and AI tooling
Important Details
- Work format: hybrid 3 days on-site
- Travel: about 25% to customer sites, mostly East Coast
Application Process
- Intro call, 30 min: discuss what you're looking for and one project you're proud of
- Technical deep dive, 60 min: walk through an LLM or agent system you built, including the trade-offs
- Demo exercise, 60 min: present a demo or architecture based on a short customer brief provided 3 days ahead, then handle a security objection
- Team conversation, 45 min: meet the Atlanta delivery engineers
- Founder conversation, 30 min: discuss values, equity, and your first 90 days
About the Company
Moring.ai helps enterprises safely deploy AI agents into production. Their forward-deployed engineers build agents with customer teams, and their AI Control Plane governs them once live. A Fortune 500 asset manager currently uses Moring, and the company is profitable quarter over quarter. The Atlanta team builds and delivers for its largest financial-services customers.
