AI/ML ENGINEER · SOFTWARE ENGINEER
Mona Arami
Building intelligent, production-oriented AI systems.
Software engineering, machine learning, generative AI, LLM applications, RAG, agentic workflows, evaluation, and modern AI system design.
AI/ML ENGINEER · SOFTWARE ENGINEER
Software engineering, machine learning, generative AI, LLM applications, RAG, agentic workflows, evaluation, and modern AI system design.
ABOUT
I’m an AI/ML Engineer and Software Engineer focused on building intelligent, production-oriented applications using machine learning, generative AI, and modern software engineering.
My background combines software engineering, data science, and applied AI, with experience in Python, SQL, machine learning, APIs, and data-driven application development. My current work focuses on LLM applications, AI agents, retrieval-augmented generation, model evaluation, MLOps, and the backend services required to operate AI workflows.
I have also contributed to applied deep-learning research in computer vision and fish ecology, including work connected with the U.S. Geological Survey and the Community for Data Integration.
I’m currently interested in mid-level AI/ML Engineer, AI Software Engineer, and Software Engineer opportunities in the Denver area and remote.
CORE FOCUS
AI Engineering
LLMs · RAG · AI Agents · Tool Calling · Vector Databases · LLM Evaluation
Machine Learning
Deep Learning · Computer Vision · TensorFlow · PyTorch · Hugging Face
Software Engineering
Python · REST APIs · SQL · Git · Backend Development · System Design
Production AI
MLOps · Model Serving · Monitoring · Evaluation · Retrieval Pipelines
FEATURED AI ENGINEERING
Current AI engineering work focused on an end-to-end application platform for building and operating LLM-powered workflows. The work centers on agentic systems, retrieval, model evaluation, and the backend services needed to move AI workflows toward production.
Current work · Technical details and public repository will be added as available.
Contributed to applied AI research using deep learning and computer vision for individual fish recognition and population assessment, supporting scalable approaches to ecological research and citizen science.
View USGS report →APPLIED AI RESEARCH
Applied deep learning and computer vision in real-world ecological research focused on identifying individual variation and population structure in native brook trout.
View publication →Applied AI research exploring how machine learning and computer vision can support scalable citizen-science data collection and analysis in fish ecology.
View USGS report →CONTINUOUS LEARNING
Selected recent credentials supporting my work in modern AI engineering, large language models, and agentic systems.
Focused on building effective agent skills and working with modern agentic AI patterns.
Focused on adapting large language models through fine-tuning workflows, data preparation, and model evaluation.