Forward‑Deployed Enterprise AI Engineer

Moring AI; Atlanta (GA); On-site; USD 120,000 - 180,000; Full time

Job summary

Moring.ai is hiring an AI Engineer to build AI applications for enterprise customers and ship them into production. You’ll work across both product and platform layers, owning features end to end and shipping inside customers’ environments with strong engineering fundamentals.

You’ll collaborate with senior engineers on architecture, embeddings, retrieval, and model integration, focusing on reliability, cost, latency, and security while embedding with client teams to fit their security

Qualifications

• 3–6 years of software engineering experience shipping production systems.
• Strong Python and the habits of a production engineer: testing, code review, observability.
• Solid AWS experience and familiarity with infrastructure-as-code.
• Recent, demonstrable AI depth with agents, RAG pipelines, or LLM applications hands-on.
• Experience with enterprise customers and embedding AI into client environments.

Responsibilities

• Build AI capabilities: agents, agentic workflows, and LLM-powered features from prototype to production.
• Deploy with enterprise customers: embed with client teams, integrate with systems, and consider security models.
• Build the platform underneath: model-access layer, agent orchestration, and evaluation harness.
• Own your work: design through deployment, write tests and observability to enable fast yet reliable shipping.

Skills

Software engineering

AWS (ECS/Lambda/IAM)

Infrastructure as code

Python

APIs

Distributed systems

Agent framework

Model APIs

RAG pipelines

LLM concepts

Tools

Terraform

CDK

LangGraph

CrewAI

Job description

Moring.ai is hiring an AI Engineer to build AI applications for enterprise customers and ship them into production. You’ll work across both product and platform layers, owning features end to end and shipping inside customers’ environments with strong engineering fundamentals.

You’ll collaborate with senior engineers on architecture, embeddings, retrieval, and model integration, focusing on reliability, cost, latency, and security while embedding with client teams to fit their security