Senior AI Platform Architect

Moring Ai•Raleigh, NC

1d•$160,000 - $180,000•Onsite

About The Position

Moring.ai is hiring a hands-on Senior AI Platform Architect to own the architecture of an AI platform for a large financial services enterprise. The platform enables the customer's developers to build and deploy Python-based agentic AI workflows that call LLMs, use tools, and run on Kubernetes. Its capabilities include an LLM gateway, agent runtime, MCP servers, skills registry, retrieval, evaluation, observability, and enterprise identity and governance controls. The architect will make platform design decisions, write architecture documents, review designs and code, and build reference implementations. The role also involves presenting and defending the architecture to customer architects, platform teams, security teams, and model risk reviewers. The platform is built on AWS and Kubernetes for a regulated enterprise with restricted egress. The position is based 100% onsite in Raleigh, North Carolina, with travel of up to 10% to customer sites.

Requirements

10–12 years of software engineering experience, with substantial experience building cloud, developer, container, or internal platforms, or working as a software architect with deep platform knowledge.
Very strong Kubernetes experience, including designing and operating production workloads and understanding networking, scheduling, security, multi-tenancy, and operators.
Very good AWS experience, including EKS, IAM, VPC networking, private endpoints, and Terraform or CDK; experience building and operating a cloud platform.
Solid understanding of AI platform concepts, including LLM gateways, agent runtimes, MCP, RAG and retrieval, skills and tool registries, evaluations, and AI observability.
Strong distributed systems, API design, and security fundamentals.
Comfort with Python sufficient to build reference implementations and review code.
Ability to write clear design documents and defend trade-offs before a client's chief architect.
Authorization to work in the United States (GC/EAD/US Citizen).

Nice To Haves

Experience working inside large enterprises, preferably in regulated industries such as financial services, insurance, or healthcare.
Experience with Bedrock AgentCore or a comparable agent runtime.

Responsibilities

Own the end-to-end architecture of the AI platform, including its gateway, agent runtime, MCP layer, skills and asset registry, retrieval, evaluations, observability, identity, and governance.
Design how agents run on Kubernetes, including packaging, scheduling, scaling, isolation, networking, state and memory, versioning, promotion, and rollback.
Design the AWS foundation, including EKS, IAM, networking, private endpoints, and infrastructure as code, for a regulated enterprise with restricted egress.
Define the identity model for agents, including agent authentication, acting on behalf of users without exceeding their access, and audit trails.
Define the developer experience and guardrails for taking an agent from an idea to deployment within the customer's organization.
Set patterns for reusable, versioned, and governed MCP servers, skills, tools, and prompts.
Write architecture documents and reference implementations, and review team designs and code against them.
Present the architecture to customer architects, platform teams, and security and model risk reviewers, incorporating their constraints into the design.
Evaluate AI platform capabilities, including model providers, agent frameworks, runtimes, and registries, and decide what belongs in the platform.
Mentor senior engineers and improve the team's platform engineering practices.