Hanna Moazam
Senior Solutions Architect at Databricks
United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolHanna Moazam is a Senior Solutions Architect with nine years of experience building data and AI solutions across cloud platforms, currently driving architecture and customer success at Databricks after roles at Microsoft. A Carnegie Mellon computer science graduate, she blends hands-on engineering, MLOps automation, and consultative design to help enterprises turn data into actionable insights. Her background spans research and teaching in deep learning, applied AI internships in media, and production-focused contributions to open-source ML tooling—most recently automating PyPI release workflows for the DSPy framework. Known for bridging models-to-production, she emphasizes robust release pipelines, metadata/versioning hygiene, and pragmatic ML deployments. Colleagues value her mentorship, public speaking, and ability to translate complex ML concepts into business outcomes. Her profile reflects a rare mix of academic rigor and operational craftsmanship that accelerates AI adoption at scale.
9 years of coding experience
6 years of employment as a software developer
Bachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at Carnegie Mellon University in Qatar
English, Urdu
Stackoverflow
58reputation
3kreached
3answers
1question
Github Skills (22)
github-ci10
dsym10
python10
setuptools10
cicd10
release-management10
build-automation10
githubaction-workflow10
git9
machine-learning8
testing8
natural-language-processing7
nlp7
python-venv6
python-modules6
Programming languages (5)
Github contributions (5)
stanfordnlp/dspy
Apr 2023 - Apr 2025
DSPy: The framework for programming—not prompting—language models
Role in this project:
MLOps Engineer & Automation Engineer
MLOps Engineer & Automation EngineerContributions:7 reviews, 21 PRs, 27 pushes in 2 years
Contributions summary:Hanna contributed to the DSPy framework by implementing automation for releases to PyPI, including deployments to test-PyPI and integration tests for validation. They also made changes to the codebase to include trust_remote_code=True to a dataset for skipping prompts, and updated the versioning and metadata configuration. The commits demonstrate a focus on build processes, package management, and ensuring the correct versioning and release workflows for the project.
language-model
hmoazam/stanford-dsp
Apr 2023 - Dec 2024
𝗗𝗦𝗣: Demonstrate-Search-Predict. A framework for composing retrieval and language models for knowledge-intensive NLP.
Contributions:139 pushes, 10 branches, 149 tags in 1 year 8 months
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