AI/ML Software Engineer

Mfinite Consulting•Annapolis, MD

1d•Remote

About The Position

Mfinite Consulting is seeking an experienced AI/ML Software Engineer to join our team. The successful candidate will build software tools that incorporate AI/ML techniques to automate tasks, assist internal users, and improve user-facing services. This includes work in RPA, chatbot development, knowledge retrieval, translation, transcription, redaction, document analysis, document generation, agentic coding, and data processing.

Requirements

Bachelor's degree in Engineering, Computer Science, Data Science, Mathematics, or a related field
At least 3 years of experience in data science, machine learning, or applied AI development
At least 3 years of experience in software engineering, architecture, or web development
SQL and relational database systems (e.g., PostgreSQL)
Fine-tuning small language models or embedding models
Open-source software project contributions
Graph databases (e.g., Neo4j, Apache AGE)
Designing and implementing multi-agent or task-oriented AI systems
Embedding models, vector similarity, re-ranking, and graph retrieval techniques
Version control (Git), containerization (Docker), service-oriented architectures
LLM integration (API-based and local deployment)
Validating AI-generated outputs and integrating AI tools into production pipelines
Proficiency in Python for backend services, APIs, middleware, and data pipelines
Hybrid cloud environments and distributed systems
Threading, asynchronous processing, and queues in backend servers
React and Microsoft Teams Toolkit for chatbot UI development
Classical NLP and LLM-based approaches
Data science and LLM-related libraries in Rust or other performance-oriented languages

Responsibilities

Design and build software systems integrating AI/ML techniques for automation and user assistance.
Collaborate with team members to define agent architectures, workflows, and system design decisions.
Evaluate and select appropriate approaches for tasks, including LLM-based and non-LLM techniques.
Develop and implement testing and evaluation pipelines for AI/ML systems.
Generate and utilize synthetic data for evaluation and benchmarking.
Support deployment of AI/ML applications in hybrid cloud environments.
Work with containerized applications (Docker) for reliable deployment and updates.
Optimize systems for environments with limited computational resources.
Document system designs, workflows, and technical decisions.
Stay informed on advancements in AI/ML and apply them within project constraints.