Nameer

keeper.ai

Keeper • Part time

Feb 2025 - Present • 1 yr 7 mos

Worked on AI engineering, LLM-based workflows, and backend systems for an AI-driven matchmaking startup. Contributed across the stack, including backend APIs, LLM orchestration, AI agent workflows, vector database infrastructure, cloud deployment, and production reliability. Took ownership of infrastructure-heavy work involving GCP, GKE, and Milvus, while also helping improve code quality, architecture, and production stability. Contributed to team-level AI agent workflows where the agent functioned as an active engineering team member, communicating through Slack and GitHub, responding to requests, handling software development tasks, reviewing pull requests, and supporting the team’s development process. Helped build practical agentic automation that connected LLM capabilities with real engineering workflows rather than isolated individual productivity tools.

Python Developer / AI LLM Engineer

Turing Intelligence • Full time

Dec 2022 - Present • 3 yrs 9 mos

Worked on creating and evaluating complex coding tasks used to train and assess advanced large language models. Designed programming challenges, reviewed model-generated code, analyzed correctness, and helped improve LLM performance on software engineering and problem-solving tasks. Contributed to evaluation workflows focused on code quality, reasoning, edge cases, test coverage, and task difficulty. Built expertise in LLM evals, coding benchmarks, prompt/task design, and judging AI-generated solutions against expected behavior.

Machine Learning Engineer

Arbisoft • Full time

May 2021 - Feb 2023 • 1 yr 9 mos

Worked as a Machine Learning Engineer on a cutting-edge EdTech AI Tutor solution, contributing to AI/ML features designed to support personalized learning experiences. Helped build and improve components involving data processing, model-backed functionality, backend integration, and intelligent tutoring workflows. Collaborated with technical leads and product teams to translate learning-focused requirements into practical AI features. Contributed to feature development, experimentation, debugging, and implementation quality while working in a production-oriented engineering environment.