AI Engineer - Forward Deployed

Moring Ai•Atlanta, GA

4d•Onsite

About The Position

Moring.ai builds enterprise AI that runs in production for regulated industries like financial services. We deliver AI applications and agents directly into our customers' own cloud environments, integrated with their existing systems. As an early-stage, lean, and profitable company, the work you ship reaches serious enterprises quickly. This role is for an AI Engineer focused on building AI applications for enterprise customers and deploying them into production. The ideal candidate has 3-6 years of software engineering experience with recent, hands-on AI development, including building agents, RAG systems, and LLM-powered features. We prioritize strong engineering fundamentals with practical AI depth, as the current challenges in AI lie in engineering aspects like reliability, cost, latency, security, and evaluating non-deterministic systems. You will work across the full stack, developing AI capabilities within the product and building the underlying platform primitives. You will own features end-to-end and grow alongside senior engineers. This is an individual contributor role where leadership is demonstrated through building. This is a forward-deployed role, meaning you will work directly with enterprise customers, embedding with their teams to ensure our AI solutions function within their specific systems and security constraints. Learnings from these engagements will be fed back into product improvements.

Requirements

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 skills and production engineering habits (testing, code review, observability).
Good grounding in APIs, data, and distributed systems.
Recent, demonstrable AI depth (within the last year) through hands-on building of agents, RAG pipelines, or LLM applications.
Working experience with at least one agent framework (e.g., LangGraph, CrewAI) and major model APIs (Anthropic, OpenAI, Bedrock).
Practical understanding of LLM behavior, including context management, token economics, cost/latency trade-offs, and testing non-deterministic systems.
Understanding of RAG with a focus on retrieval and its measurement.
Willingness to work directly with enterprise customers and communicate trade-offs clearly.

Nice To Haves

Experience integrating with 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.
Customer-facing, solutions engineering, or technical consulting exposure with enterprise customers.

Responsibilities

Build AI capabilities, including agents, agentic workflows, and LLM-powered features from prototype to production.
Develop retrieval strategies, including chunking and embedding, vector and hybrid search, and quality measurement.
Integrate models and tools such as Anthropic, OpenAI, and Bedrock model APIs, and manage tool/data access via the Model Context Protocol (MCP).
Deploy AI solutions with enterprise customers by embedding with client teams to understand their workflows and systems.
Integrate AI solutions with enterprise systems, including identity and SSO (SAML/OIDC), enterprise APIs, data warehouses, and systems of record.
Deploy into customer environments (AWS accounts, private networks) with a focus on their security models.
Support demos and working sessions with client engineers and business stakeholders.
Translate field learnings, such as integration patterns, recurring requests, and platform gaps, into product improvements.
Contribute to the development of platform primitives like the model-access layer, agent orchestration, and evaluation harness.
Implement tracing, cost and latency dashboards, and quality monitoring for AI systems.
Own features from design through deployment and operation.
Write tests, evaluations, and observability components to enable fast and confident shipping.