AI Engineer

Nasdaq•Atlanta, GA

1d•Hybrid

About The Position

We’re looking for an AI Engineer to design and deliver secure, scalable applications that bring modern AI into enterprise environments. In this role, you’ll apply technologies like LLMs, RAG, vector search, and agentic workflows to improve search, automation, and decision-making across the business. This is a hands-on role focused on applying modern AI capabilities in practical ways, not building or training foundational models from scratch. We’re looking for someone who can turn AI into useful products, workflows, and internal capabilities that are secure, scalable, and ready for production. You’ll fit right in if you’re a hands-on engineer who enjoys turning emerging AI capabilities into practical, production-grade software. You’re curious, collaborative, and comfortable working across experimentation, system design, and delivery. You know how to pair strong software fundamentals with modern AI tools to build secure, scalable solutions that solve real business problems.

Requirements

Bachelor’s degree in Computer Science, Engineering, Artificial Intelligence, or a related field, or equivalent practical experience.
5+ years of software engineering experience, including proven experience building AI or LLM-enabled applications in production environments.
Strong hands-on experience with RAG architectures, embeddings, vector databases, semantic search, and cloud AI platforms such as AWS Bedrock or equivalent.
Experience designing agentic workflows, integrating MCP tools and servers, and building AI experiences for chat or conversational user interfaces.
Strong programming and system design skills in one or more modern languages such as C#, Java, Python, or JavaScript/TypeScript, with experience building APIs and cloud-native backend services.

Nice To Haves

Experience building AI solutions in enterprise, fintech, or other regulated environments.
Familiarity with frameworks such as Semantic Kernel, LangChain, LangGraph, Microsoft Agent Framework, or similar orchestration frameworks.
Experience evaluating and operating AI systems in production, including grounding, relevance, latency, safety, observability, and reusable platform capabilities.

Responsibilities

Design and build AI-powered applications that use LLMs to improve user workflows, search, automation, and decision support.
Develop and optimize RAG-based solutions using enterprise data, embeddings, and vector search to enable relevant, context-aware experiences.
Integrate AI capabilities through AWS Bedrock or similar platforms, including prompt engineering, guardrails, and safety controls that improve quality and reliability.
Build conversational and agentic experiences that coordinate tools, functions, and multi-step reasoning, including MCP tools, functions, and server integrations.
Partner with product, architecture, and engineering teams to deliver scalable, maintainable AI systems and establish strong evaluation, observability, and monitoring practices.