AI/ML Software Engineer
Location: Remote
Employment Type: W2 Contract
Rate: $110/hour
Duration: Multi-year contract
Overview
We are seeking an experienced AI/ML Software Engineer to design and build software solutions that leverage artificial intelligence and machine learning to automate targeted tasks, enhance internal workflows, and improve user-facing services. This role supports initiatives for the Maryland Judiciary, with a focus on high-accuracy, production-grade systems built within defined technical constraints.
Key Responsibilities
· System Design & Collaboration:
o Work within established constraints regarding infrastructure, programming languages, and model selection
o Contribute to technical decision-making related to data processing, retrieval strategies, and system integration
o Collaborate with team members to define agent architectures, workflows, and system design decisions
o Evaluate and select appropriate approaches for given tasks, including determining when to use LLM-based versus non-LLM techniques
o Designing and building software systems that integrate AI/ML techniques to automate tasks, assist internal users, and improve user-facing services.
· Testing, Evaluation, and Quality Assurance:
o Assist in the design and implementation of testing and evaluation pipelines for AI/ML systems
o Develop unit and integration tests for AI-enabled workflows and data pipelines
o Generate and utilize synthetic data to support evaluation and benchmarking efforts
o Contribute to improving system performance, including accuracy, latency, and cost efficiency
· Deployment & Operations:
o Support deployment of AI/ML applications within a hybrid cloud environment
o Work with containerized applications to ensure reliable deployment and updates.
o Optimize systems for environments with limited computational resources, including minimal GPU availability
· General Responsibilities:
o Deliver production-grade systems aligned with defined requirements, while supporting iterative improvement of evolving tools
o Document system designs, workflows, and technical decisions as required
o Stay informed on relevant advancements in AI/ML and apply them where appropriate within project constraints
Required Qualifications:
Preferred Skills and Experience:
· SQL and relational database systems (e.g., PostgreSQL)
· Fine-tuning small language models or embedding models
· Contributing to or maintaining open-source software projects
· Graph databases or graph extensions (e.g., Neo4j, Apache AGE)
· Designing and implementing multi-agent or task-oriented AI systems
o Embedding models, vector similarity, re-ranking, and graph retrieval techniques in RAG systems
o Version control systems (e.g., Git), containerization technologies (e.g., Docker), and service-oriented architectures
o Collaborating with large language models (LLMs), including both API-based integration and local deployment
o Validating AI-generated outputs, mitigating hallucinations, and integrating AI tools into production service pipelines
o Ability to:
§ Understand data structures, algorithms, and clean coding principles
§ Select and apply appropriate techniques (LLM and non-LLM) based on task requirements
§ 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.
§ Design and implement AI/ML systems that operate effectively on complex, inconsistent, or evolving datasets while balancing accuracy, latency, and cost (token consumption)
§ 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:
o Hybrid cloud environments and distributed system considerations
o Threading, asynchronous processing, and queues in backend servers
o React and Microsoft Teams Toolkit for developing chatbot user interfaces
o Non-llm data analysis techniques for structured, semi-structured, and unstructured data
o Classical natural language processing (NLP) techniques in addition to LLM-based approaches
o Data science and LLM-related libraries in Rust or other performance-oriented programming languages