Momin Qureshi

• Contributing as one of three core developers building and scaling Tyce’s AI-powered document assistant. • Implemented the ”Tyce Agent” using PydanticAI, including fine-tuning agent behavior, designing the agent tools, streaming responses, and optimizing • Optimized agentic RAG search by implementing contextual retrieval. • Implemented custom HTML splitter for more stable and style-aware document chunking. • Scaled data ingestion pipeline to handle 100,000+ in a few minutes by moving ingestion to AWS Lambda. • Used advanced techniques, including chunking optimization, parallel processing, and selective processing, to cutdown processing costs and time by 20% - 90%, depending on the document. • Participating in product roadmap and architecture discussions, helping shape both technical direction and user experience. • Stack: Pydantic AI, FastAPI, Python, PostgreSQL, AWS Lambda, SST, OpenAI API, Docker, CI/CD

• Architected data pipelines that curated and transformed 10 000+ multimodal samples (2M+ tokens) for training state-of-the-art LLMs using the latest training formats (RLHF, SFT, DPO, ORPO, etc.) • Fine-tuned and evaluated code-generation and chat models with Hugging Face Transformers, raising pass-@-1 accuracy to 70% and cutting hallucinations by 18%. • Built an automated evaluation harness that reduced model validation cycles by 40%. • Rolled out semi-supervised active-learning workflows and auto-labeling pipelines that slashed manual QA effort by 5x. • Led a cross-functional team of 12 data engineers, mentoring them on prompt engineering, dataset design, and scalable MLOps best practices. • Implemented robust PII and toxicity detection in Python & SQL, achieving 99.2% precision and ensuring SOC 2 / GDPR compliance. • Deployed GenAI services on Kubernetes with GitOps practices using GitHub CI/CD pipelines, streamlining production rollout and rollback. • Integrated models with external platforms like ServiceNow to enable real-time AI-assisted workflows. • Provisioned cloud infrastructure on AWS, GCP, and Azure using Infrastructure as Code (IaC) to deploy and manage GenAI training utilities-including evaluation harnesses, data pipelines, and model serving-while maintaining a 99.99% SLA.

Worked on a Propriety data platform for a global data analytics firm. • Played a key role in developing and maintaining robust cloud-based ETL pipelines for a leading fashion analytics platform, ensuring a reliable flow of 4 TB+ raw data daily. • Collaborated with a fully remote team across 3 time zones, leveraging sprint rituals and clear documentation to hit 95% of roadmap milestones on schedule. • Re-engineered the extraction component of a fashion analytics pipeline (20k+ lines), shrinking daily runtime by 36% and saving $45k per year in compute. • Built and maintained full-stack features: React dashboards for merchandising teams, backed by Django REST API endpoints and AWS API Gateway. • Ensured data integrity and performance with automated tests (PyTest + Cypress), reaching 85% coverage and daily CI runs. • Deployed micro-frontends and Python services via Docker/Kubernetes and Terraform, enabling blue-green releases with <5 min rollback.

• Delivered 100+ contracts ranging from predictive analytics to indie games. • Built real-time analytics platform on GCP handling 60k events/sec; reduced query latency from 2 s to 200 ms. • Optimized PySpark pipelines, lowering cluster costs 22% through improved partitioning.

• Improved API response time 55% and boosted uptime to 99.8% by refactoring Django ORM queries. • Migrated the website’s hosting and database from shared cPanel to AWS EC2 and Amazon RDS, improving reliability and scalability.