Syed Rizvi
@syedrizvi3
Senior AI/ML engineer specializing in LLMs, RAG, and production-scale GenAI solutions.
What I'm looking for
I am a Senior AI/ML engineer with 12 years of experience building production machine-learning and GenAI systems, focused on LLMs, RAG, agentic workflows, and scalable inference. I have launched enterprise GenAI assistants for audit, tax, and customer support that achieved roughly 70% autonomous resolution and drastically reduced response times.
My work spans recommendation engines, multilingual virtual agents, and enterprise data platforms; notable results include a recommendation engine that increased CTR by 60% and revenue per visitor by 10%, and automation initiatives that cut routine query handling by ~70%. I design domain-governed RAG stacks, implement privacy and Responsible AI guardrails, and productionize low-latency endpoints with measurable ROI dashboards.
I partner with product and stakeholder teams to drive adoption and mentor engineers, bringing hands-on expertise in FastAPI, LangChain/LangGraph, Hugging Face, vector databases, and cloud deployment to deliver safe, auditable, and high-performance AI solutions.
Experience
Work history, roles, and key accomplishments
Launched GenAI assistants and domain-governed RAG platforms for audit and tax that achieved ~70% autonomous resolution and reduced response times by 70%, while implementing privacy guardrails and autoscaled low-latency inference.
Deployed multilingual AI assistants and a RAG framework integrating LangChain and Pinecone to reach 70% autonomous inquiry resolution and cut response times by 70%, while scaling inference via FastAPI and AWS.
Built a recommendation engine and ML pipeline that lifted click-through rates by 60% and revenue per visitor by 10% through collaborative filtering, embeddings, and Google Cloud Recommendations AI.
Machine Learning Engineer
Uber
Jan 2016 - Dec 2018 (2 years 11 months)
Co-built recommendation and support automation components for Uber Eats using gradient-boosted trees and early deep learning, improving home-feed CTR and reducing support handling time.
Developed a Location Relevance search model and data pipelines that improved booking conversion and reduced irrelevant search results through feature engineering and experimentation.
Education
Degrees, certifications, and relevant coursework
University of Washington
Master of Science, Computer Science
2014 - 2016
Master of Science in Computer Science completed with coursework and projects focused on machine learning and data-driven systems.
University of Washington
Bachelor of Science, Computer Science: Data Science
2009 - 2012
Bachelor of Science in Computer Science with a major emphasis in Data Science, covering foundational algorithms, statistics, and data engineering.
Availability
Location
Authorized to work in
Job categories
Skills
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