AI/ML Software Engineer

Position Overview

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.

Key Responsibilities

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

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)

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

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

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

Required Qualifications

• 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

Benefits

• Opportunity for advancement
• Training & development

Flexible work from home options available.