AI Engineer – LLMs, Agentic Workflows & MCP

International Logic Systems, Inc.•Fairfax, VA

6h•Hybrid

About The Position

International Logic Systems (ILS) is seeking an AI Engineer to design, develop, and implement AI solutions using Large Language Models (LLMs), agentic workflows, and Model Context Protocol (MCP)-based integrations. The role focuses on building and deploying AI-powered applications and orchestrating tools, APIs, and data sources into reliable, scalable, and secure systems. The position is based in Fairfax, Virginia, with a hybrid arrangement requiring two onsite days per week at ILS headquarters. Candidates must be local to the Washington, DC metropolitan area. The role is full-time and involves collaboration with cross-functional teams in an Agile/Scrum environment.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related field.
5–9 years of professional experience in software engineering, AI engineering, or applied machine learning roles.
Hands-on experience working with LLM APIs to build AI-powered applications.
Experience designing and implementing agent-based workflows and agentic AI systems.
Strong understanding of prompt engineering, tool calling, and structured outputs.
Familiarity with RAG architectures and vector databases.
Experience with MCP or similar standards for integrating AI agents with external tools and enterprise systems.
Experience with at least one major cloud platform and exposure to others, including AWS, Microsoft Azure, and Google Cloud.

Nice To Haves

Strong programming skills in Python and/or Java.
Experience developing backend services using Spring Boot, including REST APIs and microservices.
Familiarity with Spring AI or similar frameworks for integrating LLM capabilities into Java applications.
Experience with Docker and containerized application deployments.
Basic frontend development experience with React and/or Angular for AI-powered applications.
Experience working in regulated environments, such as government, financial services, or healthcare.

Responsibilities

Design, develop, and implement agentic AI systems that reason, plan, and execute tasks using LLMs.
Implement MCP integrations connecting AI agents with tools, APIs, and enterprise systems.
Develop, deploy, and maintain LLM-powered applications, including AI copilots, conversational systems, and intelligent automation tools.
Build prompt pipelines, tool-calling workflows, and multi-step reasoning systems for complex business use cases.
Develop backend services using Java (Spring Boot) and/or Python to expose AI capabilities through secure, scalable APIs.
Leverage Spring AI or similar frameworks to integrate LLM capabilities into enterprise applications.
Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge sources.
Integrate AI solutions with cloud-native services and platforms, including AWS, Microsoft Azure, and Google Cloud.
Deploy and maintain AI services, ensuring scalability, reliability, security, and performance.
Collaborate with cross-functional teams in an Agile/Scrum environment to deliver AI-powered solutions.
Monitor, evaluate, and optimize AI systems for latency, cost efficiency, accuracy, and output quality.