AI/ML Software Engineer

Position Overview

We are seeking an experienced AI/ML Software Engineer to design and build production-grade tools for the Maryland Judiciary. The successful candidate will integrate AI/ML techniques to automate tasks, assist internal staff, and enhance the digital experience for external users. This role focuses on developing intelligent systems within hybrid cloud environments, often optimizing for resource-constrained settings.

Key Responsibilities

• Design and implement software systems that integrate AI/ML techniques to automate specific tasks with high accuracy.
• Develop agent architectures, workflows, and system designs in collaboration with cross-functional teams.
• Build and deploy containerized applications within hybrid cloud environments.
• Determine optimal approaches for technical challenges, including the selection between LLM-based and non-LLM techniques.
• Optimize system performance for environments with limited computational resources and minimal GPU availability.
• Create robust testing and evaluation pipelines, including the generation of synthetic data for benchmarking.
• Develop unit and integration tests for AI-enabled workflows and backend data pipelines.
• Document all system designs, technical decisions, and workflows to ensure production-grade quality.
• Contribute to high-level decision-making regarding data processing and retrieval strategies.

Minimum Requirements

• Bachelor of Science in Computer Science, Engineering, Data Science, Mathematics, or a related field.
• At least 3 years of experience in data science, machine learning, or applied AI development.
• At least 3 years of experience in software engineering, architecture, or web development.
• Proven proficiency in Python for developing production-grade backend services, APIs, and middleware.

Preferred Experience

• Collaborating with Large Language Models (LLMs) via API-based integration and local deployment.
• Implementing RAG systems using embedding models, vector similarity, re-ranking, and graph retrieval.
• Working with SQL/relational databases (e.g., PostgreSQL) and graph databases (e.g., Neo4j, Apache AGE).
• Fine-tuning small language models or embedding models.
• Designing multi-agent or task-oriented AI systems.
• Utilizing version control (Git) and containerization (Docker) within service-oriented architectures.