AI/ML Software Engineer - Hybrid ANNAPOLIS, MD

Novalink Solutions LLC•Annapolis, MD

1d•Hybrid

About The Position

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.

Requirements

Bachelor of Science in Engineering, Computer Science, Data Science, or Mathematics, or a related field (as determined by the AOC).
At least three (3) years’ experience in data science, machine learning, or applied AI development.
At least three (3) years’ experience in software engineering, architecture, or web development.

Nice To Haves

Experience with SQL and relational database systems (e.g., PostgreSQL).
Experience with fine-tuning small language models or embedding models.
Experience with contributing to or maintaining open-source software projects.
Experience with graph databases or graph extensions (e.g., Neo4j, Apache AGE).
Experience with designing and implementing multi-agent or task-oriented AI systems.
Experience with embedding models, vector similarity, re-ranking, and graph retrieval techniques in RAG systems.
Experience with version control systems (e.g., Git), containerization technologies (e.g., Docker), and service-oriented architectures.
Experience with collaborating with large language models (LLMs), including both API-based integration and local deployment.
Experience with validating AI-generated outputs, mitigating hallucinations, and integrating AI tools into production service pipelines.
Ability to understand data structures, algorithms, and clean coding principles.
Ability to select and apply appropriate techniques (LLM and non-LLM) based on task requirements.
Ability to develop and improve testing and evaluation pipelines for AI systems, including use of synthetic data.
Demonstrate proficiency in Python, including the ability to develop production-grade backend services, APIs, middleware, and data pipelines.
Ability to design and implement AI/ML systems that operate effectively on complex, inconsistent, or evolving datasets while balancing accuracy, latency, and cost (token consumption).
Ability to 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.
Knowledge of hybrid cloud environments and distributed system considerations.
Knowledge of threading, asynchronous processing, and queues in backend servers.
Knowledge of React and Microsoft Teams Toolkit for developing chatbot user interfaces.
Knowledge of non-llm data analysis techniques for structured, semi-structured, and unstructured data.
Knowledge of classical natural language processing (NLP) techniques in addition to LLM-based approaches.
Knowledge of data science and LLM-related libraries in Rust or other performance-oriented programming languages.

Responsibilities

Design and build software systems that integrate AI/ML techniques to automate tasks, assist internal users, and improve user-facing services.
Work within established constraints regarding infrastructure, programming languages, and model selection.
Contribute to technical decision-making related to data processing, retrieval strategies, and system integration.
Collaborate with team members to define agent architectures, workflows, and system design decisions.
Evaluate and select appropriate approaches for given tasks, including determining when to use LLM-based versus non-LLM techniques.
Assist in the design and implementation of testing and evaluation pipelines for AI/ML systems.
Develop unit and integration tests for AI-enabled workflows and data pipelines.
Generate and utilize synthetic data to support evaluation and benchmarking efforts.
Contribute to improving system performance, including accuracy, latency, and cost efficiency.
Support deployment of AI/ML applications within a hybrid cloud environment.
Work with containerized applications to ensure reliable deployment and updates.
Optimize systems for environments with limited computational resources, including minimal GPU availability.
Deliver production-grade systems aligned with defined requirements, while supporting iterative improvement of evolving tools.
Document system designs, workflows, and technical decisions as required.
Stay informed on relevant advancements in AI/ML and apply them where appropriate within project constraints.