AI Engineer

International Logic Systems, Inc.•Fairfax, VA

8d•Hybrid

About The Position

ILS Inc. is seeking an AI Engineer to design and implement next-generation AI solutions using large language models (LLMs), agentic workflows, and Model Context Protocol (MCP)-based integrations. The ideal candidate has hands-on experience building AI-powered applications (not just training models) and understands how to orchestrate tools, APIs, and data sources into reliable systems. This role requires onsite (hybrid) work in Fairfax, VA, with 2 days per week at the ILS HQ office.

Requirements

Bachelor’s degree in computer science, Engineering, or related field.
2–5 years of professional experience in software engineering, AI engineering, or applied ML roles.
Hands-on experience with LLM APIs.
Experience building agent-based workflows.
Understanding of prompt engineering, tool usage, and structured outputs.
Familiarity with RAG architectures and vector databases.
Experience with Model Context Protocol (MCP) or similar integration standards.
Experience working with at least one cloud platform and exposure to others: AWS (Lambda, Bedrock, SageMaker, S3), Azure (Azure OpenAI, Functions, Cognitive Services), Google Cloud (Gemini Enterprise Agent Platform, Vertex AI, Cloud Functions, BigQuery).

Nice To Haves

Strong programming skills in Python and/or Java.
Experience building backend services using Spring Boot (REST APIs, microservices).
Exposure to Spring AI or similar frameworks for integrating LLMs into Java applications.
Experience with Docker and containerized deployments.
Basic frontend experience (React, Angular) for AI-driven applications.
Experience working in regulated environments (government, finance, healthcare).

Responsibilities

Design and build agentic AI systems that can reason, plan, and execute tasks using LLMs.
Implement integrations using Model Context Protocol (MCP) to connect AI agents with tools, APIs, and enterprise systems.
Develop and maintain LLM-powered applications (e.g., copilots, chat systems, automation tools).
Build prompt pipelines, tool-calling workflows, and multi-step reasoning systems.
Develop backend services using Java (Spring Boot) and/or Python to expose AI capabilities via APIs.
Leverage Spring AI or similar frameworks to integrate LLMs into enterprise applications.
Implement RAG (Retrieval-Augmented Generation) pipelines using vector databases.
Integrate AI solutions with cloud-native services such as AWS, Azure, and Google Cloud.
Deploy AI services and ensure scalability, reliability, and performance.
Collaborate with cross-functional teams in an Agile/Scrum environment.
Monitor and optimize AI systems for latency, cost, and output quality.