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AI/ML Software Engineer

Strategic Solutions

AI/ML Software Engineer

Crofton, MD
Full Time
Paid
  • Responsibilities

    Benefits:

    Opportunity for advancement

    Training & development

    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

    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

    1. 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)

    1. 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

    1. 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

    1. 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

    Flexible work from home options available.