Vetted LSTM Professionals

“Built and shipped a production LLM hallucination detection and monitoring pipeline using semantic-level entropy (embedding-clustered multi-generation variance) to flag unreliable outputs in downstream automation. Implemented a scalable async architecture (FastAPI + Docker + Redis/Celery) with strong observability (structured logs + PostgreSQL) and developed evaluation loops combining controlled prompts and human review; also partnered with non-technical stakeholders on AI-driven form validation/document processing.”

“Hands-on engineer who has taken an LSTM Bitcoin forecasting model from notebook to a production-grade, monitored API (Docker/Gunicorn/Nginx, Prometheus/Grafana, blue-green rollback) delivering 99.9% availability and ~110–120ms p95 latency. Also built an RFID self-checkout prototype spanning Raspberry Pi + firmware + networking, using deep instrumentation to eliminate double-charges/timeouts (<0.1%) and reduce checkout time ~20% through idempotency, debounce logic, and hardware fixes.”

Shehab mohamed mohamed

Screened

Junior AI Engineer specializing in MLOps, LLMs, and multi-agent systems

Cairo, Egypt2y exp

“ML/AI engineer focused on production-grade systems, with experience building a low-latency multi-agent 'neural concierge' booking platform used across domains like restaurants and hospitals. Also worked on a healthcare computer vision system for nystagmus/eye-movement analysis, showing a mix of scalable LLM infrastructure, MLOps, and safety-conscious medical AI experience.”

“Built a university campus assistant chatbot (BabyJ/WWJ) using RAG and agentic routing with a FastAPI + React stack and JWT auth, focusing heavily on production concerns like latency and reliability. Uses techniques like speculative prefetching, smart intent routing, and rigorous eval/testing (golden sets, regression, edge cases) while collaborating closely with campus admin/advising teams to iterate based on real user feedback.”

Ekta Mulkalwar

Screened

Junior Machine Learning & Full-Stack Engineer specializing in applied AI systems

Los Angeles, CA2y exp

“Master’s thesis focused on building and deploying a gait-based biometric authentication system using mobile accelerometer time-series data as an alternative to passwords/2FA. Emphasized real-world robustness by addressing sensor noise and variability (phone placement, walking speed, footwear) and improving safety using biometric metrics like FAR/FRR and EER, while collaborating closely with a non-ML thesis advisor.”

“Internship-built full-stack systems spanning HR employee-record portals and internal data-quality dashboards (Flask + SQL + React), emphasizing data integrity and rapid MVP iteration. Also implemented Flask microservices with RabbitMQ for distributed task processing, addressing duplication/ordering issues with idempotency, durable queues, and correlation-ID logging; delivered quantified productivity gains for HR teams.”