Vetted Neural Networks Professionals

“Frontend engineer who led the end-to-end UI for an internal employee catalog tool at Genetec, building React/TypeScript dashboards with complex search filters. Emphasizes tight product-owner feedback loops (weekly demos), Figma-based design alignment, and disciplined delivery practices using CI/CD, automated tests, and version tagging for rollouts/reverts.”

“AI/ML Engineer who built a production RAG-based LLM system for insurance policy documents, turning thousands of messy PDFs into a searchable index using LangChain, Azure AI Search vectors, hybrid retrieval, and FastAPI. Strong focus on evaluation (MRR/precision@k/recall@k, REGAS) and performance optimization (vLLM), with prior clinical NLP experience using BERT-based NER validated on ground-truth datasets.”

“Built and deployed an end-to-end product that brings a research-paper approach into production for large-scale time-series clustering, with attention to partitioning, latency, and scalability. Also designed a Python-based backend validation service (comparing outputs to database ground truths) and handled production reliability issues by reproducing dataset-specific crashes and hardening corner-case behavior with client-friendly errors.”

“Backend/web API engineer with hands-on experience deploying .NET Core APIs to Azure App Service and stabilizing production systems through disciplined log-driven troubleshooting, environment configuration management, and SQL performance tuning (execution plans, query rewrites, indexing). Has also debugged cross-layer incidents involving DB locks and network latency, and modifies Python/XML automation scripts to meet customer-specific requirements while improving logging and performance.”

“Embedded/software candidate who built an arcade-style game on a PSoC6 ARM microcontroller using FreeRTOS and custom peripheral/protocol drivers, ultimately completing the project as the sole developer. Also brings strong systems tooling exposure (Docker-heavy coursework, Kubernetes familiarity) plus internship experience at Accenture working with CI/CD-based validation and debugging in a client environment.”

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”

“AI/ML engineer with forensic analytics and healthcare claims experience (Optum), building production LLM/RAG systems to surface context-driven fraud patterns from unstructured claim notes and explain risk to investigators. Strong in large-scale retrieval performance tuning, legacy API integration with reliability patterns (SQS, circuit breakers), and MLOps orchestration on Airflow/Kubernetes with rigorous testing, monitoring, and stakeholder-friendly interpretability.”

“Intern who built and deployed a production LLM-powered contract analysis system for finance teams: Azure Document Intelligence for text/table extraction plus Gemini prompting to surface key terms and risks via an async API and simple UI. Emphasizes reliability in production with fallbacks, guardrails against hallucinations, and operational concerns like latency/cost/versioning, delivering summaries in under 30 seconds instead of hours.”

“Built an end-to-end wafer-data anomaly detection and reporting system at Samsung using PySpark, Random Forest models, SQL, and Grafana to help engineers track faults and take corrective action. Also has strong UX prototyping and validation practices in Figma plus hands-on front-end/full-stack experience (HTML/CSS/TypeScript), including a student project recognized as best design out of 25 teams, and early-stage startup experience pivoting a product based on user interviews into a real-time in-context feedback overlay.”

“Built a production multi-agent LLM system at Live Nation on Databricks (LangGraph/LangChain) that let venue/event teams ask questions in Slack, auto-generated optimized route schedules, and produced inventory/stocking recommendations from historical SQL data and venue trends. Improved reliability by tightening prompts with strict JSON schemas, providing sample questions/SQL, and adding guardrails plus synthetic/edge-case testing, while iterating with event managers and senior VPs via prototypes and feedback loops.”

“Sales professional focused on staffing/talent operations and GTM enablement solutions, with a disciplined outbound motion (ICP, org mapping, multi-threading across calls/LinkedIn/email). Has examples of winning mid-market deals ($30K–$60K) and expanding an account to ~$52K ACV, plus building early-stage sales process rigor (MEDDICC, pipeline reviews) that produced predictable pipeline in ~90 days and shortened cycles by ~20%.”