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Hamidreza Lotfalizadeh - ML Research Engineer in Bay Area, CA

Hamidreza Lotfalizadeh

Mid-level AI/ML Engineer specializing in LLM agents, RAG, and ML systems

Inertia SystemsPurdue UniversityBay Area, CA6 Years ExperienceMid LevelWorks On-Site

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About

At Inertia Systems, built a production LLM-powered ingestion pipeline that converts heterogeneous sources (PDF/JSON/IFC/SQL and financial tables) into standardized text and uses GraphRAG to construct a knowledge graph with verified dependency relationships. Also has hands-on HPC orchestration experience with SLURM, including creating a custom wrapper process manager to improve resource utilization under restrictive scheduling policies.

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Key Strengths

  • Built and deployed an LLM-driven ingestion pipeline to create a knowledge graph from heterogeneous data sources (PDF/JSON/IFC/SQL)
  • Solved inconsistent LLM output formatting by constraining generation to a small set of reusable patterns and iterating patterns via supervised review
  • Optimized SLURM-constrained scheduling by building a wrapper process manager that forks child processes to better utilize allocated resources
  • Designs reliable AI workflows via decomposable steps, guardrails/validators, and fallback hierarchies
  • Uses structured evaluation practices (unit tests, golden datasets, trajectory testing) and considers robustness/efficiency/calibration metrics

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Experience

ML Research EngineerIndiana University · Apr 2025 – Present
Graph Systems Specialist (Consulting)Inertia Systems, Inc. · Jul 2025 – Sep 2025contract
AI/ML Engineering InternEli Lilly and Company · May 2024 – Aug 2024internship
ML Systems Developer (PhD Research)Purdue University · Oct 2022 – Dec 2024
Data Science Engineer (PhD Research)Purdue University · Dec 2019 – Apr 2022

Education

Purdue Universitydoctorate, Computer Engineering (2024)

Languages

English

Publications

1 publication

Graph machine learningMachine learning systemsDistributed MLGPU optimization

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Hamidreza LotfalizadehMid-level AI/ML Engineer specializing in LLM agents, RAG, and ML systems