AI/ML Software Engineer

The AI/ML Software Engineer will design and build advanced software systems that leverage artificial intelligence and machine learning to automate narrowly defined tasks with high accuracy, enhance internal workflows, and improve user-facing digital services for the Maryland Judiciary. This role focuses heavily on applied AI engineering, including LLM-based systems, retrieval-augmented generation (RAG), agent-based architectures, and intelligent automation. The engineer will contribute to building scalable, production-grade solutions such as chatbots, document processing systems, transcription and translation tools, and AI-driven research platforms., 1. System Design & Engineering Design and develop software systems integrating AI/ML capabilities into enterprise applications Build intelligent agents for: Knowledge retrieval (RAG, hybrid search) Deep research (GraphRAG, structured reasoning) Document analysis, generation, and redaction Translation and transcription Work within defined constraints (infrastructure, programming languages, model selection) Evaluate and select appropriate techniques (LLM vs traditional ML vs rules-based approaches) Define agent architectures, workflows, and system integrations Collaborate with cross-functional teams on system design and technical decisions

2. AI/ML Testing, Evaluation & Optimization Design and implement testing and evaluation pipelines for AI/ML systems Develop unit and integration tests for AI workflows and data pipelines Generate and leverage synthetic datasets for benchmarking Continuously improve: Model accuracy System latency Cost efficiency Conduct comparative evaluations of AI approaches (e.g., RAG strategies, embeddings, model variants)

3. Deployment & Platform Engineering Deploy AI/ML applications in hybrid cloud environments Work with containerized applications (Docker/Kubernetes) Optimize systems for resource-constrained environments (limited GPU availability) Ensure reliable CI/CD pipelines and production stability

4. Intelligent Automation & RPA Develop AI-enhanced robotic process automation (RPA) tools Implement batch processing workflows using local or hosted LLMs Build reporting pipelines and analytics for automation usage and efficiency

5. Documentation & Continuous Improvement Document system architecture, workflows, and technical decisions Stay current with advancements in AI/ML and apply innovations appropriately Deliver production-ready systems while supporting iterative enhancements Core Solution Areas You Will Work On Internal and external chatbot platforms Retrieval-Augmented Generation (RAG) systems Graph-based research systems (GraphRAG) AI-powered transcription and translation services PII detection and automated redaction tools Document analysis, extraction, and generation systems AI-assisted coding and workflow automation

Bachelor's degree in Computer Science or related field 5-8+ years of software engineering experience (senior-level preferred) Strong experience building production-grade AI/ML systems Hands-on experience with: LLMs (OpenAI, open-source models, or similar) RAG architectures and vector databases Python and modern backend frameworks Experience with: API design and microservices architecture Data processing pipelines Containerization (Docker) Preferred Qualifications Experience with: Graph-based retrieval (GraphRAG, knowledge graphs) NLP, document processing, and entity extraction Speech-to-text and multilingual systems Familiarity with: Hybrid cloud environments Low-resource AI optimization techniques Experience in: Government, legal, or judiciary systems (highly desirable) Knowledge of: Data privacy, PII handling, and compliance frameworks Key Skills AI/ML Engineering (LLMs, NLP, RAG, Agents) Software Development (Python, APIs, Microservices) Data Engineering & Processing System Design & Architecture Testing & Evaluation of AI Systems DevOps & Containerization What Success Looks Like Delivery of scalable, secure, and high-performing AI systems Measurable improvements in automation, efficiency, and user experience Reliable deployment of AI tools within constrained environments Continuous innovation aligned with evolving AI capabilities Flexible work from home options available.