Vetted NumPy Professionals

Mohammed Syed

Screened

Mid-level AI Engineer & Researcher specializing in healthcare AI and multimodal LLM systems

Remote2y exp

“Backend/ML engineer focused on clinical AI transparency who built ShifaMind, an explainability-enforced clinical ML system using UMLS/MIMIC-IV/PubMed data with RAG, GraphSAGE, and cross-attention. Demonstrated strong production engineering via FastAPI API design and safe migrations (feature flags/shadow inference), plus HIPAA-aligned auth/RLS patterns; also delivered a real-time comet detection system reaching 97.7% accuracy.”

Hemanth Suddala

Screened

Mid-level GenAI Engineer specializing in LLM automation, RAG, and document intelligence

Boca Raton, FL3y exp

“Built and deployed a production GenAI resume screening and matching system for Florida Atlantic University, focused on improving recruiter efficiency and search relevance. Demonstrates strong RAG engineering (embeddings, query rewriting, metadata filtering, threshold tuning) plus practical reliability work (grounding constraints, fallbacks, and evaluation using real user queries) using Python REST APIs and orchestration frameworks like LangChain and LlamaIndex.”

“Data engineer who has owned high-volume production pipelines end-to-end (200–300 GB/day) on AWS, implementing strong data quality/observability and achieving 99.9% reliability while cutting data issues ~33%. Also built a large-scale external data collection system ingesting millions of records/day with anti-bot/rate-limit handling and backfill tooling, and shipped a versioned REST service exposing curated Snowflake data to downstream teams.”

“Built and shipped a personal LLM-powered news aggregation platform (Clear Brief) that scrapes ~200 articles per cycle, clusters them into ~15–30 consolidated stories, and supports on-demand deep dives via a Next.js API route. Emphasizes production-minded reliability (token/cost controls, timeouts, graceful frontend degradation) and database-backed orchestration using SQLite with retry + exponential backoff for burst processing.”

Tamanna Nandlal Choithani

Screened

Entry-level Full-Stack Engineer specializing in AI and distributed systems

California, USA1y exp

“Full-stack engineer who built an AI-based inventory/procurement query system at Botlily/Botlerly using Flask and Google Sheets as a live knowledge base, overcoming Sheets latency with caching and structured in-memory models. Demonstrated strong LLM product engineering (40% accuracy improvement via preprocessing/prompting) and customer-driven iteration with bar/restaurant owners, evolving the tool into a more comprehensive inventory management and forecasting solution.”

Yanet Fernandez

Screened

Junior Data Analyst specializing in healthcare analytics

Fort Myers, FL2y exp

“Analytics/data professional with hands-on experience turning messy semi-structured CRM JSON data in Snowflake into clean reporting layers using SQL and validation logic. Brings a practical mix of data engineering, Python automation, metric design, and stakeholder alignment to improve reporting accuracy and speed of decision-making.”

“Analytics professional with hands-on experience turning messy CRM and campaign data into standardized reporting datasets using SQL and Python. At EcoSolutions/EcoServices, they built reusable KPI pipelines, dashboards, and metric definitions that reduced manual reporting and improved time to conversion by roughly 55% through better segmentation and campaign optimization.”

“Analytics-focused candidate with hands-on experience building SQL and Python pipelines for messy, high-volume data across e-commerce, marketing, healthcare, and regional resource allocation use cases. Particularly strong in turning ambiguous business problems into operational metrics and trusted Power BI reporting, including CLV, patient retention, and vulnerability-based segmentation.”

“Analytics-focused candidate with hands-on experience building compliance and contract utilization reporting from messy contract, vendor, subcontractor, and payment data. They combine SQL and Python automation to improve reporting speed and accuracy, and show strong stakeholder discipline through validation sessions, documentation, and dashboard adoption.”

RAUNAQ BEDI

Screened

Entry-level Software Engineer specializing in AI, data engineering, and cloud DevOps

San Francisco, CA1y exp

“Product-minded full-stack engineer with strong React/TypeScript, serverless AWS, and Postgres depth, highlighted by owning real-time personalization and onboarding experiences at mParticle. Stands out for combining deep performance debugging with measurable product impact—improving activation by 28%, reducing time-to-insights by 35%, and building reusable internal platform primitives adopted by 12 teams.”

“Graduate-level candidate who uses AI as a disciplined engineering assistant rather than an autonomous replacement, with hands-on experience coordinating manual multi-agent coding workflows across planning, implementation, and testing. They emphasize scoped execution, clear constraints, and human ownership of final merges, suggesting a thoughtful and practical approach to AI-augmented software development.”

“ML engineer with hands-on experience building production AI systems spanning agentic AI, RAG, LLM automation, fraud detection, and predictive analytics. At Origami Risk, they designed and implemented an enterprise RAG platform end to end using LangChain, LangGraph, vector search, and AWS Bedrock to improve internal knowledge retrieval, reduce manual effort, and raise response quality across teams.”

“Software engineer at Expedera working at the intersection of deep learning compilers and neural processor hardware, focused on making customer models run efficiently across custom HW architectures. Particularly notable for building a zero-to-one multi-chip scheduler in a Python + C++ stack and for translating complex model optimization problems into customer-facing performance gains for hardware deployments, including autonomous driving use cases.”