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. Location: Remote (Annapolis, MD - Requires 2 initial days onsite) Salary Range: $120,000 - $149,000 USD Industry: Public Sector / Government 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. t least 3 years of experience in data science, machine learning, or applied AI development. t 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. Macpower Digital Assets Edge