Nebiyou Hailemariam — Resume

Master's in IT, Applied Machine Learning • GPA: 3.93

Focus on deep learning, NLP, and AI systems development.

Bachelor of Science, Computer Science • GPA: 3.8

Strong foundation in software engineering, algorithms, and system design.

Top 3% of Freelance Developers

Recognized as one of the top 3% of freelance developers on Toptal's exclusive talent network.

Foundation: Introduction to LangGraph

Certificate in LangGraph foundations for building stateful, multi-actor applications with LLMs.

Software Engineer II, AI at Motive

Aug 2024 – Present

• Building and maintaining a multi-tenant car dealership platform used by 300+ dealerships across the U.S. and Canada, serving millions of users, leveraging Python (FastAPI), Ruby on Rails, and React.
• Developed an agentic conversational system using LangChain, LangSmith, FastAPI, and Pydantic, capable of creating and updating webpages, performing content analytics, and generating SEO-optimized blog posts, enabling dealership admins to scale content automation.
• Fine-tuned lightweight reranker models using PyTorch, Sentence Transformers, Hugging Face, and Vertex Workbench to improve search result relevance and boost conversion rates.
• Set up training and deployment pipelines on GCP Vertex AI (Cloud Storage, Artifact Registry, Vertex Training, Vertex Model, Vertex Endpoint) and used Weights & Biases for experiment tracking and monitoring.
• Utilized the pytest framework to test AI microservice functionality, structuring tests with the Arrange–Act–Assert pattern for clarity and maintainability.

Software Engineer, AI at eezly

Aug 2024 – March 2025

• Worked on eezly, a grocery price comparison application used by over 30,000+ users, leveraging ASP.NET Core Web API, Python (FastAPI), PyTorch, and cloud-based microservices to build scalable, AI-driven app.
• Built a Recipe Recommendation System using LangChain, OpenAI, and the Recipe1M+ dataset, creating a Retrieval-Augmented Generation (RAG) system to suggest recipes based on the products users purchase. Incorporated the Weaviate vector database to enhance search and recommendation.
• Employed PyTorch and Hugging Face to train hierarchical machine-learning models for classifying retail products from various stores (e.g., Walmart) into aisles, categories, and subcategories.
• Integrated Gorse, a recommender system, and contributed to open-source recommender systems.
• Designed and implemented RESTful APIs for inventory management using n-tier architecture and developed a single-page application with React.js.
• Implemented OAuth 2.0 client-credential flow using OpenIddict for secure machine-to-machine communication, Single Sign-On (Firebase, Cognito), and ASP.NET Core Identity for user management.