We are seeking an Applied AI Engineer to design, build, and deploy production-ready AI solutions with a focus on agentic workflows and autonomous orchestration. In this role, you will work across the full lifecycle of AI system development, including requirements definition, data processing, algorithm integration, application development, deployment, and optimization. You will develop AI-enabled applications and services that leverage modern machine learning techniques, large language models (LLMs), and scalable cloud-based architectures to solve complex, multi-step reasoning tasks.
You will be responsible for building API-driven services, implementing Retrieval-Augmented Generation (RAG) pipelines, and deploying containerized applications across cloud and on-premise environments. Success in this role requires strong software engineering skills, a deep understanding of agentic design patterns, and the ability to communicate complex technical solutions effectively within a collaborative, fast-paced, and highly regulated environment.
Required
- BS Degree in Computer Science, Engineering, or a related technical field
- 3 years of related experience
- Proficiency in Python
- Experience building, deploying, and maintaining secure REST APIs
- Experience with Docker and containerized application deployment
- Experience building AI agents and advanced RAG pipelines, including tool integration, reasoning loops, and data retrieval across database types (e.g., SQL, vector, document, graph).
- Familiarity with multi-agent architecture patterns
- Must have the ability to obtain and maintain a security clearance
Desired
- Experience working with LLMs across the full lifecycle, from integration and orchestration to fine-tuning, distillation, and prompt/system design
- Experience deploying AI solutions in secure or regulated cloud environments (e.g., Azure GCC High, AWS GovCloud, or equivalent)
- Experience designing human-in-the-loop workflows that blend AI decision support with expert oversight.
- Ability to translate stakeholder needs into technical requirements and produce clear engineering documentation for both technical and non-technical audiences.
- Familiarity with multi-agent architecture patterns
- Experience with Model Context Protocol (MCP) or API workflows designed for AI tool-calling.
