Senior AI Platform Architect

Moring Ai•Dallas, TX

1d•$160,000 - $180,000•Onsite

About The Position

Moring.ai is hiring a Senior AI Platform Architect to own the end-to-end architecture of an AI platform for a large financial services enterprise. The platform enables the enterprise’s developers to build and deploy Python-based agentic AI workflows that call LLMs, use tools, and run as agents on Kubernetes. It includes an LLM gateway, agent runtime, MCP servers for tools and data, a skills registry, retrieval, evaluation, observability, and identity and governance controls. This is a hands-on architecture role involving design documents, code reviews, reference implementations, and architectural decisions that guide the engineering team. The architect will also present and defend the design to customer architects and security teams, incorporating enterprise constraints into the platform. The role includes evaluating AI platform capabilities, mentoring senior engineers, and improving platform engineering practices. The position is based 100% onsite in Dallas, Texas, with travel of up to 10% to customer sites. It is full-time and offers a salary of $160,000–$180,000, plus a $25,000 bonus and stock options, based on experience and match to the role. 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 experience, including EKS, IAM, VPC networking, private endpoints, and Terraform or CDK; prior 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.
Experience working inside large enterprises; regulated-industry experience is preferred. Ability to understand how enterprise policies affect system design.
Ability to write clear design documents and defend trade-offs in front of a client's chief architect.
Must be authorized to work in the United States (GC/EAD/US Citizen).

Nice To Haves

Experience in regulated industries, preferably financial services, insurance, or healthcare.
Experience with agent runtimes such as Bedrock AgentCore or comparable platforms.

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 for a regulated enterprise with restricted egress, including EKS, IAM, networking, private endpoints, and infrastructure-as-code.
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 the path from an idea to a deployed agent, with appropriate guardrails and minimal friction.
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 the customer's chief architect, platform teams, and security and model risk reviewers, and incorporate their constraints.
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.