AI Engineer - Forward Deployed

Atlanta, United States | Posted on 08/08/2026

Moring.ai builds enterprise AI that runs in production for regulated industries - financial services and other compliance-heavy organizations. We ship AI applications and agents live inside our customers' own cloud environments, integrated with the systems they already run - not slide ware, not demos. We're early-stage, lean, and profitable, so the work you ship reaches serious enterprises fast.

The Role

We're hiring an AI Engineer to build AI applications for enterprise customers and ship them into production. You've spent 3-6 years as a software engineer building real systems, and in the last year you've gone hands-on with AI - building agents, RAG systems, and LLM-powered features, not just reading about them.

We're not looking for years of GenAI experience - nobody has that. We want strong engineering fundamentals with recent, real AI depth on top, because the hardest problems in AI right now are engineering problems: reliability, cost, latency, security, and evaluating systems that don't behave the same way twice.

You'll work across both sides of our stack: shipping AI capabilities in the product and building the platform primitives underneath them. You'll own features end to end and grow quickly alongside senior engineers who make the deepest architecture calls. This is an IC role - you'll lead by building.

This is also a forward-deployed role. We build with our enterprise customers, not just for them - so you'll spend real time embedded with client teams, making our AI work inside their systems and security constraints, then bringing what you learn back into the product.

What You'll Do

Build AI capabilities.

• Build and ship agents, Agentic workflows, and LLM-powered features - from prototype to production.
  
• Build retrieval that works: chunking and embedding strategies, vector and hybrid search, and honest measurement of retrieval quality.
  
• Integrate models and tools: Anthropic, OpenAI, and Bedrock model APIs, and tool/data access via the Model Context Protocol (MCP).
  

Deploy with our enterprise customers.

• Embed with client teams to understand their workflows and systems, then build the AI solution that fits their environment.
  
• Integrate with the systems enterprises actually run: identity and SSO (SAML/OIDC), enterprise APIs, data warehouses, and systems of record.
  
• Deploy into customer environments - their AWS accounts and private networks - with their security model in mind, not just ours.
  
• Support demos and working sessions with client engineers and business stakeholders.
  
• Turn field learnings - integration patterns, recurring requests, platform gaps - into product improvements.
  

Build the platform underneath.

• Contribute to our model-access layer, agent orchestration, and evaluation harness - the primitives every AI feature builds on.
  
• Add tracing, cost and latency dashboards, and quality monitoring so AI changes ship with evidence, not vibes.
  

Own your work.

• Take ownership of features from design through deployment, and operate what you build.
  
• Write the tests, evals, and observability that let you ship fast with confidence.
  

Requirements

What We're Looking For

• 3-6 years of software engineering experience shipping production systems - backend, platform, or full-stack.
  
• Solid AWS experience (ECS/Lambda, IAM, networking) and familiarity with infrastructure-as-code (Terraform or CDK).
  
• Strong Python and the habits of a production engineer: testing, code review, and observability.
  
• Good grounding in APIs, data, and distributed systems.
  
• Recent, demonstrable AI depth - within roughly the last year is fine; we care that it's real. You've built agents, RAG pipelines, or LLM applications hands-on and can walk us through what you built and what broke. Production work, serious side projects, and open source all count.
  
• Working experience with at least one agent framework (LangGraph, CrewAI, or comparable) and the major model APIs (Anthropic, OpenAI, Bedrock).
  
• Practical understanding of LLM behaviour: context management, token economics, cost/latency trade-offs, and testing non-deterministic systems.
  
• Understanding of RAG with a focus on retrieval - and how to measure whether it's actually working.
  
• Willingness to work directly with enterprise customers and communicate trade-offs in plain language.
  

Nice to Have

• Experience integrating with enterprise systems - SSO, enterprise APIs, data platforms, or systems of record.
  
• Familiarity with MCP, including its current limitations and security considerations.
  
• TypeScript for product-surface work.
  
• Prior startup experience - you've shipped with small teams and moved fast.
  
• Customer-facing, solutions engineering, or technical consulting exposure with enterprise customers.
  

How You Work

• You own outcomes, not tickets - you'll take a problem, shape it, build it, and run it.
  
• You're pragmatic about new technology: excited by the ecosystem, honest about trade-offs.
  
• You ship fast and still sleep at night, because you build in the tests, evals, and observability that let you.
  
• You listen before you build, and you're comfortable in a customer's room as well as in the codebase.
  

Details

Travel: Up to 25% to customer sites.

Work authorization: You must be authorized to work in the United States; visa sponsorship is not available for this role at this time.