Vetted Snowflake Professionals

“Backend Python engineer who built core services for a telecom automation engine monitoring thousands of routers in real time and auto-generating support tickets. As the sole Intelygenz engineer on the project, they diagnosed a costly Terraform/GitLab CI/CD resource-leak issue in AWS and implemented a cleanup redesign that eliminated orphaned resources and reduced client cloud spend. Also shipped applied-AI ticket triage suggestions via API integration and built an end-to-end Gmail-to-ticket ingestion workflow.”

“Backend-focused Python engineer who has owned production FastAPI services deployed on Kubernetes, including CI/CD (GitLab CI to ECR) and GitOps delivery via ArgoCD/Helm. Has hands-on experience with complex reliability and infrastructure work—solving data inconsistency with validation/partial-data paths, fixing K8s liveness issues via lazy loading, and supporting a phased cloud-to-on-prem migration with dual-writes and monitoring. Also built Kafka-based real-time ingestion consumers handling bursty, high-throughput traffic with async processing and topic/retention tuning.”

“Built and deployed a production LLM-powered support assistant for customer support agents using a RAG architecture over internal docs and past tickets, with human-in-the-loop review. Demonstrates strong applied LLM engineering focused on real-world constraints (hallucinations, latency, cost) using routing to smaller models, reranking, caching, and rigorous evaluation/monitoring (offline eval sets, A/B tests, KPI tracking).”

“AI/ML Engineer at Fifth Third Bank who has shipped production fraud detection and risk analysis systems combining ML models with LLM-powered insights/explanations, including real-time monitoring, drift detection, and automated retraining under regulatory explainability constraints. Also built a hybrid-retrieval internal knowledge-base QA system (+20% top-5 relevance) and delivered a customer support chatbot that reduced first response time by 30% through strong stakeholder collaboration.”

“Built and deployed a real-time hospital readmission risk prediction system at NYU Langone Health, combining structured EHR data with BERT-based NLP on clinical notes and serving predictions to clinicians via Azure ML and FHIR APIs. Emphasizes production reliability and clinical trust through SHAP-based explainability and robust healthcare data preprocessing, and reports a 22% reduction in 30-day readmissions.”

“Frontend engineer (Cyderes) specializing in security analytics/SOC dashboards, building complex multi-tenant React + TypeScript interfaces for near real-time authentication and MFA monitoring. Known for scaling quality via strict TS, shared contracts, CI-enforced multi-level testing, and performance optimization, plus pragmatic incremental refactors and gated rollouts that protect active customer workflows.”

“Customer-facing software engineer who rapidly turns business requirements into Figma prototypes and PoC applications, using workflow prioritization and frequent client reviews to stay aligned. Has hands-on experience integrating with existing authentication/user APIs, building MongoDB-backed caching, and implementing robust fallback/retry mechanisms. Comfortable working on-site with customers and resolving production issues in AWS (e.g., DNS/EC2 traffic routing) in collaboration with DevOps.”

“Built and deployed a RAG-based student/faculty support chatbot at a university that answers from official syllabus/policy documents and now supports 4,000+ students while reducing repetitive support requests. Hands-on with LangChain, LangGraph, and CrewAI to orchestrate reliable agentic workflows, with a strong focus on testing/monitoring in production and cross-functional delivery (e.g., marketing analytics automation at Steve Madden).”

“Built and deployed a production RAG system for financial/compliance teams using GPT-4, Claude, and local models to retrieve and summarize thousands of internal documents with strong security controls (role-based retrieval, PII masking). Drove significant operational gains (30+ hours/week saved, ~35% productivity lift, ~45% faster responses) and orchestrated end-to-end ingestion/embedding/index refresh pipelines with Airflow, S3, and SageMaker while partnering closely with compliance stakeholders on auditability and traceability.”

“NLP/ML engineer with hands-on healthcare and support-ticket text experience, building clinical-note structuring and semantic linking systems using spaCy, BERT clinical embeddings, and FAISS. Emphasizes production-grade delivery (Airflow/Databricks, PySpark, Docker, AWS/FastAPI/Lambda) and rigorous validation via clinician-labeled datasets, retrieval metrics, and user feedback.”

“ML engineer/data scientist who deployed a production credit risk + insurance claims triage platform at Hartford Financial, combining XGBoost default prediction with BERT-based document classification. Demonstrated strong MLOps by cutting inference latency to sub-500ms and building drift monitoring plus automated retraining/deployment pipelines (MLflow, CloudWatch, GitHub Actions, SageMaker) with human-in-the-loop review and SHAP-based explainability for underwriting adoption.”

“AI/ML engineer who led production deployment of a multimodal (text/video/image) RAG system on GCP using Gemini 2.5 + Vertex AI Vector Search, scaling to 10M+ documents with sub-second latency and +40% retrieval accuracy. Strong MLOps/orchestration background (Kubernetes, CI/CD, Airflow, MLflow) with proven impact on reliability (75% fewer incidents) and deployment speed (92% faster), plus experience delivering explainable ML (XGBoost + SHAP + Tableau) to non-technical retail stakeholders.”

“ML/NLP practitioner with healthcare payer experience at HCSC, focused on connecting messy unstructured clinical notes to structured claims/provider data to improve fraud-analytics workflows. Has hands-on experience fine-tuning transformers in AWS SageMaker, building large-scale embedding search with FAISS, and implementing robust entity resolution using golden datasets, precision/recall calibration, and production monitoring for drift.”

“Backend/data engineer in healthcare who built an AWS-based clinical analytics platform from scratch (DynamoDB/S3/Airflow/dbt) with sub-second clinician query goals, 99.9% uptime, and HIPAA-grade controls (KMS encryption, IAM RBAC, audit trails). Also modernized ML delivery by replacing a manual 4-hour deployment with a 30-minute Docker/GitHub Actions CI/CD pipeline using parallel runs, parity testing, and rollback, and caught critical EHR data edge cases (date formats/timezones) that could have impacted patient care.”

“ML/LLM engineer with production experience building medical RAG systems to automate chart review, including retrieval + re-ranking and rigorous evaluation. Notably uncovered errors/bias in physician-curated ground truth by tracing answers back to source note chunks and presented evidence to an academic partner, accelerating deployment. Also built a RAG-based FAQ chatbot for a health insurance company and delivered it to non-technical stakeholders via demos.”

“Software developer with hands-on experience owning customer-facing work end-to-end (requirements, implementation, testing, and feedback-driven iteration) using Python and React.js. Also described remodeling an internal legacy page/tool to improve performance and accuracy, and has exposure to microservices and RabbitMQ plus ETL-based system work.”

“Backend/DevOps-leaning engineer managing multiple customer service platforms end-to-end (requirements through deployment). Built an in-house Python monitoring/alerting solution for Salesforce-to-Java contact sync jobs (Snowflake dependencies) that increased uptime ~60%, and helped modernize delivery by moving the team from manual releases to automated Jenkins-based deployments while coordinating an Oracle EBS→Fusion transition with business/data/IT stakeholders.”

“Graduate Assistant Intern at Friends University who built and deployed a GenAI-driven requirement understanding system that automates extraction and semantic grouping of technical requirements from large unstructured documents. Demonstrates strong LLM engineering rigor (golden datasets, regression testing, post-processing validation) and production-minded delivery using LangChain/LlamaIndex orchestration, FastAPI microservices, Docker, and cloud deployment.”

“Built and deployed a production RAG-based enterprise document intelligence platform for financial/compliance/operational documents on AWS (Spark/Glue ingestion, embeddings + vector DB, LangChain orchestration, REST APIs on Docker/Kubernetes). Deep hands-on experience orchestrating multi-step and multi-agent LLM workflows (LangChain, LangGraph, CrewAI) with strong focus on grounding, evaluation, observability, and cost/latency optimization, and has partnered closely with non-technical finance/compliance teams to drive adoption.”

“Built and deployed a production LLM/RAG system at CVS to automate clinical documents, addressing PHI compliance, retrieval accuracy, and latency; achieved a 35–40% reduction in review effort through chunking and FP16/INT8 optimization. Also has experience translating AI outputs into actionable insights for non-technical stakeholders (sports analysts).”

“Clinical data scientist who has built an agentic LLM-powered literature review assistant (with RAG-style storage/retrieval) to identify predictors for downstream predictive modeling. Also delivered a patient-focused progression analysis model using Databricks + Airflow orchestration, partnering closely with clinicians to define targets and validate that model insights aligned with clinical expectations.”

“ML/AI engineer with production experience in high-scale banking fraud detection at Truist, building an end-to-end pipeline (Airflow/AWS Glue/Snowflake, PyTorch/sklearn) with automated retraining and Kubernetes-based deployment; delivered measurable gains (22% fewer false positives, 15% higher recall) and reduced manual ops ~40%. Also partnered with clinicians at Kellton to deploy an LLM system for summarizing/classifying clinical notes, improving review time and decision speed.”