AI/ML Software Engineer

The AI/ML Software Engineer will build software tools that incorporate AI/ML techniques to automate narrowly defined tasks with high accuracy, assist internal users with their job functions, and improve the experience external users have when interacting with the Maryland Judiciary. This includes, but is not limited to, RPA work, building or refining chatbots, incorporating AI/ML into reporting tools, building llm agents for knowledge retrieval, deep research, translation, transcription, redaction, document analysis, document generation, agentic coding, and data processing.

1. Bachelor of Science in Engineering, Computer Science, Data Science, or Mathematics, or a

related field (as determined by the AOC).

2. The AOC prefers Offeror proposed resource(s) to have the following qualifications:
3. At least three (3) years experience in data science, machine learning, or applied AI

development.

1. At least three (3) years experience in software engineering, architecture, or web

development.

Experience with:

(1) SQL and relational database systems (e.g., PostgreSQL)

(2) Fine-tuning small language models or embedding models

(3) Contributing to or maintaining open-source software projects

(4) Graph databases or graph extensions (e.g., Neo4j, Apache AGE)

(5) Designing and implementing multi-agent or task-oriented AI systems

(6) Embedding models, vector similarity, re-ranking, and graph retrieval techniques in RAG systems

(7) Version control systems (e.g., Git), containerization technologies (e.g., Docker), and service-oriented architectures

(8) Collaborating with large language models (LLMs), including both API-based integration and local deployment

(9) Validating AI-generated outputs, mitigating hallucinations, and integrating AI tools into production service pipelines

1. Ability to:

(1) Understand data structures, algorithms, and clean coding principles

(2) Select and apply appropriate techniques (LLM and non-LLM) based on task requirements

(3) Develop and improve testing and evaluation pipelines for AI systems, including use of synthetic data

(4) Demonstrate proficiency in Python, including the ability to develop productiongrade backend services, APIs, middleware, and data pipelines.

(5) Design and implement AI/ML systems that operate effectively on complex, inconsistent, or evolving datasets while balancing accuracy, latency, and cost (token consumption)

(6) Collaborate with team members to define system architecture, agent workflows, and data pipelines while working in constrained environments, including limited GPU availability and predefined infrastructure

1. Knowledge of:

(1) Hybrid cloud environments and distributed system considerations

(2) Threading, asynchronous processing, and queues in backend servers

(3) React and Microsoft Teams Toolkit for developing chatbot user interfaces

(4) Non-llm data analysis techniques for structured, semi-structured, and unstructured data

(5) Classical natural language processing (NLP) techniques in addition to LLM-based approaches

(6) Data science and LLM-related libraries in Rust or other performance-oriented programming languages