AI/ML Software Engineer

E Logic•Annapolis, MD

20h

About The Position

E-Logic is seeking a highly skilled AI/ML Software Engineer to support the Maryland Judiciary. This role focuses on designing and developing AI-driven solutions that enhance internal operations and improve user-facing services. The selected candidate will work on innovative projects involving machine learning, automation, and intelligent systems, helping modernize digital services and streamline complex workflows. This is a long-term opportunity (up to 5 years) supporting a high-impact government program.

Requirements

Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or related field.
Strong programming experience in Python.
Experience building backend systems, APIs, or data pipelines.
Understanding of algorithms, data structures, and software engineering principles.

Nice To Haves

3+ years of experience in: Machine learning, data science, or applied AI. Software engineering or system architecture.
Experience with: SQL and relational databases (e.g., PostgreSQL). AI/ML frameworks and LLM integrations. Containerization (Docker) and version control (Git). Vector search, embeddings, and retrieval systems. Graph databases (e.g., Neo4j).
Familiarity with: Hybrid cloud environments. NLP techniques (LLM and non-LLM approaches). Multi-agent or task-based AI systems.

Responsibilities

Design and build AI/ML-powered applications to automate business processes and improve user experiences.
Develop solutions such as: Chatbots (internal and external). Document processing and analysis tools. Knowledge retrieval systems (RAG, search optimization). AI-driven automation (RPA).
Contribute to architecture decisions, including model selection and system integration.
Collaborate with cross-functional teams to define workflows and system design.
Develop and maintain testing frameworks for AI/ML systems.
Build unit and integration tests for data pipelines and AI workflows.
Evaluate system performance (accuracy, latency, cost efficiency).
Use synthetic data for benchmarking and validation.
Deploy AI/ML applications in hybrid cloud environments.
Work with containerized solutions (e.g., Docker).
Optimize applications for environments with limited compute resources.
Deliver production-grade systems aligned with evolving requirements.
Document system architecture, workflows, and technical decisions.
Stay current with AI/ML advancements and apply them effectively.