Senior AI Platform Architect

Moring Ai•Alpharetta, GA

4d•$160,000 - $180,000•Onsite

About The Position

Moring.ai is an early-stage, lean, profitable company building production enterprise AI for regulated and compliance-heavy industries. Its AI applications and agents run inside customers' own cloud environments and integrate with their existing systems. The Senior AI Platform Architect will own the end-to-end architecture of an AI platform for a large financial services enterprise. The platform enables developers to build and deploy Python-based agentic AI workflows that call LLMs, use tools, and run on Kubernetes. Platform capabilities include an LLM gateway, agent runtime, MCP servers, skills registry, retrieval, evaluation, observability, identity, and governance controls. This is a hands-on architecture role involving design documents, code review, reference implementations, and architecture decisions that guide the engineering team. The architect will present and defend the design to customer architects and security teams, incorporate enterprise constraints, evaluate new AI platform capabilities, and mentor senior engineers. The position is based 100% onsite in Alpharetta, Georgia, with up to 10% travel to customer sites. Candidates must be authorized to work in the United States.

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 running production workloads and understanding networking, scheduling, security, multi-tenancy, and operators.
Very good AWS knowledge, 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 such as Bedrock AgentCore or comparable systems, 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 architectural 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 financial services, insurance, or healthcare.

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 for moving from an idea to a deployed agent with appropriate guardrails and minimal friction.
Set reusable, versioned, and governed patterns for MCP servers, skills, tools, and prompts.
Write architecture documents and reference implementations, and review designs and code against them.
Present the architecture to the customer's chief architect, platform teams, security reviewers, 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.