Pre-screened and vetted in the Bay Area.
Junior Machine Learning Engineer specializing in multimodal AI and audio deepfakes detection
“Internship experience building production-oriented AI systems, including a real-time voice scam/spoof detector (RawNet + AASIST) hardened for noisy audio via aggressive augmentation and Zoom-based noise simulation, evaluated with EER on clean and wild datasets. Also built an LLM-driven UI automation agent using Playwright for apps like Linear/Notion with modular tool design, unit tests, and replayable scripted scenarios, and has AWS Step Functions experience orchestrating Lambda/Cognito workflows.”
Senior Applied AI/ML Engineer specializing in GenAI, LLMs, RAG and agents
“Applied AI/ML Engineer at JPMorgan Chase who led a banker-facing LLM chatbot from an OpenAI-API POC to a production RAG workflow, including hallucination mitigation, automated evaluation in SageMaker, and operational monitoring with Dynatrace. Also delivers external technical education—hosted a hands-on Grace Hopper Celebration 2025 workshop teaching LangChain/LangGraph agentic workflows.”
Mid-level AI/ML Engineer specializing in generative AI, NLP, and MLOps
“ML/AI engineer with hands-on ownership of production GenAI and computer vision systems, spanning experimentation, deployment, monitoring, and iterative optimization. Stands out for shipping an enterprise RAG platform that cut manual review by 50% and a defect detection pipeline that reduced report generation from 15 minutes to under 1 second while maintaining high uptime and strong operational discipline.”
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and MLOps
“AI/ML engineer who has shipped production AI systems end-to-end, including an automated multi-channel (Gmail/WhatsApp/voice) candidate interviewing workflow and an enterprise RAG knowledge search platform. Demonstrates strong production rigor (monitoring, A/B tests, guardrails, schema validation, shadow testing) with quantified impact: ~60–70% reduction in interview evaluation time and ~20–30% relevance gains in RAG retrieval.”
Mid-level Machine Learning Engineer specializing in Generative AI and RAG systems
“GenAI/LLM engineer with production deployments in both fintech and retail: built an AI-powered mortgage document analysis/automated underwriting pipeline at Fannie Mae (OCR + custom LLM) cutting underwriting review from 3–4 hours to under an hour with privacy-by-design controls. Also helped build Sephora’s GenAI product advisory bot using LangChain-orchestrated RAG (Azure GPT-4, Azure AI Search, MySQL HeatWave vector search), focusing on grounding, evaluation, and compliance-aware architecture choices.”
Junior AI Engineer specializing in GenAI, RAG, and agentic systems
Director-level AI Engineering Manager specializing in healthcare payer AI and search/NLP
Principal AI/ML Architect & Senior Data Scientist specializing in financial fraud and risk
Mid-level Data Engineer specializing in ML, OCR, and cloud-native pipelines
Director-level AI/ML Engineering Leader specializing in GenAI, Agentic AI, and AI Governance
Mid-level Computational Biologist & Healthcare AI Developer specializing in LLM agents
Entry AI Application Engineer specializing in GPU infrastructure benchmarking
Mid-level NLP Engineer specializing in LLMs, RAG, and applied computational linguistics
Senior Data Scientist / AI & ML Engineer specializing in agentic AI and generative AI
Mid-level Full-Stack Software Engineer specializing in ML platforms and observability
Mid-level Machine Learning Engineer specializing in LLM training and FinTech ML systems
Staff AI/MLOps Engineer specializing in cloud-native LLM and document intelligence platforms
Mid-level AI/ML Engineer specializing in MLOps, real-time data platforms, and generative AI
Mid-level AI/ML Engineer specializing in NLP, RAG, and cloud MLOps
Staff AI/MLOps Engineer specializing in cloud-native LLM and document intelligence platforms
Mid-level AI Software Engineer specializing in GenAI platforms for finance and healthcare
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and applied AI