AI Engineer - Forward Deployed
Forward-deployed AI engineer who builds and ships LLM applications — agents, RAG, and AI features — for enterprise customers in regulated industries like financial services, embedding with client teams (up to 25% travel). Core stack: Python, AWS, agent frameworks (LangGraph/CrewAI), and Anthropic/OpenAI/Bedrock APIs.
- Compensation
- Not disclosed
- Location
- Atlanta, United States
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 3+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 3–6 years of software engineering experience shipping production systems (backend, platform, or full-stack)
- Authorized to work in the United States (no visa sponsorship)
- Solid AWS experience (ECS/Lambda, IAM, networking)
- Familiarity with infrastructure-as-code (Terraform or CDK)
- Strong Python with production engineering habits: testing, code review, observability
- Good grounding in APIs, data, and distributed systems
- Recent hands-on AI depth: built agents, RAG pipelines, or LLM applications
- Working experience with at least one agent framework (LangGraph, CrewAI, or comparable)
- Experience with major model APIs (Anthropic, OpenAI, Bedrock)
- Practical understanding of LLM behaviour: context management, token economics, cost/latency trade-offs, testing non-deterministic systems
- Understanding of RAG with a focus on retrieval and how to measure whether it works
- Willingness to work directly with enterprise customers and communicate trade-offs in plain language
Nice to have
- Experience integrating enterprise systems (SSO, enterprise APIs, data platforms, systems of record)
- Familiarity with MCP, including its limitations and security considerations
- TypeScript for product-surface work
- Prior startup experience shipping with small teams
- Customer-facing, solutions engineering, or technical consulting exposure with enterprise customers
Full description
About Us
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 DoBuild 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.