Vetted Snowflake Professionals

“AI/LLM engineer with recent production experience at UnitedHealth Group building an end-to-end RAG system over structured EMR data and unstructured clinical notes, including evidence retrieval, GPT/LLaMA-based reasoning, and a validation layer for reliability. Strong in orchestration (Kubeflow/Airflow/MLflow), prompt engineering for noisy healthcare text, and rigorous evaluation/monitoring with gold-standard benchmarking, plus close collaboration with clinical operations stakeholders.”

“AI/LLM engineer with production experience building secure, scalable compliance-focused generative AI systems (GPT-3/4, BERT) including RAG over internal regulatory document bases. Has delivered end-to-end pipelines on AWS with PySpark/Airflow/Kubernetes/FastAPI, emphasizing privacy controls, monitoring, and iterative evaluation (A/B testing). Also partnered closely with bank compliance officers using prototypes to refine NLP summarization/classification and reduce document review time.”

“At Dreamline AI, built and productionized an AWS-based incentive intelligence platform that uses Llama-2/GPT-4 to extract eligibility rules from unstructured state policy documents into structured JSON, then processes them with Glue/PySpark and serves results via Lambda/SageMaker/API Gateway. Designed state-specific ingestion connectors plus schema validation and automated checks/alerts to handle frequent policy/format changes without breaking the pipeline, and partnered with business/analytics stakeholders to deliver interpretable eligibility decisions via explanations and dashboards.”

“AI/ML engineer with production experience building a RAG-based internal analytics assistant (Databricks + ADF ingestion, Pinecone vector store, LangChain orchestration) deployed via Docker on AWS SageMaker with CI/CD and MLflow. Strong focus on real-world constraints—latency/cost optimization (LoRA ~60% compute reduction), hallucination control with citation grounding, and enterprise security/governance. Previously at Intuit, delivered an interpretable churn prediction system (PySpark/Databricks, Airflow/Azure ML) that improved retention targeting ~12%.”

“ML/LLM engineer at Charles Schwab who built a production loan-advisor chatbot integrated with internal knowledge and loan-calculator APIs, adding strict numeric validation to prevent rate hallucinations and optimizing context to control costs. Also runs ~40 Airflow DAGs orchestrating retraining/ETL/drift monitoring with an automated Snowflake→SageMaker→auto-deploy pipeline, and uses rigorous testing plus canary rollouts tied to business metrics and compliance constraints.”

“Backend/Python engineer who owned an end-to-end FastAPI + AWS internal natural-language document Q&A system (Textract extraction, embeddings/vector DB, LLM integration) with strong focus on reliability and latency. Hands-on with Kubernetes + GitOps (Argo CD, Helm, rolling updates/auto-rollback) and built/optimized Kafka streaming pipelines using Prometheus/Grafana. Also supported a zero-downtime on-prem to cloud migration with parallel run and gradual traffic cutover.”

“Enterprise healthcare GTM/CS leader who owned large accounts end-to-end (sales through renewal), including scaling a hospital network rollout to 51 of 52 facilities in ~3 months. Built an automated customer-feedback and response system (“Schism”) using n8n + ChatGPT that cut CS workload by ~50% and informed product priorities, and has experience driving cross-functional EHR-related deployments and GTM analytics (Metabase/Snowflake/Firebase).”

“Backend engineer with experience at HSBC and Machinations who has delivered major production performance wins (cutting large trade-file upload times from ~13–15s to ~2s) using chunked parallel processing with strong reliability controls. Also built and shipped an applied AI RAG workflow using Langflow + Cohere embeddings + FAISS with hosted/local LLM fallbacks (Hugging Face, Ollama) and production-grade guardrails, observability, and evaluation.”

“AI/ML engineer who built a production RAG-based internal document intelligence assistant (LangChain + Pinecone) to let employees query enterprise reports in natural language. Demonstrated hands-on pipeline orchestration with Apache Airflow and tackled real production issues like retrieval grounding and latency using tuning, caching, and token optimization, while partnering closely with non-technical business stakeholders through iterative demos.”

“QA automation engineer with Barclays digital banking experience who owned an end-to-end regression suite across UI, API, and database layers (Selenium/TestNG, REST Assured, SQL) and integrated it into CI/CD (Jenkins/GitLab). Known for preventing high-impact financial defects like duplicate transaction postings by adding backend SQL validations, negative/edge-case coverage, and converting production issues into automated regression tests; also strong in Cypress flake reduction using cy.intercept/cy.session and stable selectors.”

“Full-stack engineer who has shipped and owned production analytics dashboards using Next.js App Router + TypeScript, combining server components for data-heavy pages with client components for interactive charts/filters. Also built a Temporal-orchestrated payment reconciliation workflow with versioning, idempotency, and exponential-backoff retries, and has hands-on Postgres query/index optimization using EXPLAIN ANALYZE.”

“Software engineer with experience at Goldman Sachs and Arizona State University’s Learning Engineering Institute, shipping production backend systems including a vendor equities invoice-generation service designed for extensibility across multiple vendors. Built Django REST + PostgreSQL backends with JWT auth and Pytest coverage, and delivered data-heavy, responsive Angular dashboards; also has exposure to AWS EC2 deployments and GitLab CI/CD automation.”

“Data engineer/backend data services practitioner with Bank of America experience building real-time and batch transaction-monitoring pipelines and APIs (Kafka + databases, REST/GraphQL). Highlights include a reported 45% response-time improvement through performance optimizations and use of Delta Lake schema evolution plus CI/CD (GitHub Actions/Jenkins) and operational reliability patterns like CloudWatch monitoring and dead-letter queues.”

“Data engineer focused on building reliable, production-grade pipelines and external data collection systems on AWS (S3/Lambda/SQS/Glue/EMR) using PySpark/SQL, serving curated datasets to Snowflake/Redshift for finance and fraud teams. Has operated a large-scale crawler ingesting millions of records/day with anti-bot tactics, schema versioning/quarantine, and CloudWatch/Datadog monitoring, and also shipped a versioned REST API with caching and query optimization.”

“Data engineer at AT&T focused on large-scale telecom (5G/IoT) data platforms, owning end-to-end pipelines from Kafka/Azure ingestion through Databricks/Delta Lake transformations to serving analytics and ML. Has operated at very high volumes (~50+ TB/day) and delivered measurable performance gains (25–30% faster processing) plus improved reliability via Airflow monitoring, robust data quality checks, and resilient external data collection patterns (rate limiting, retries, dynamic schemas).”

“Built and deployed an LLM-powered customer support assistant (“Notable Assistant”) focused on automating common post-customer queries while maintaining multi-turn context and meeting scalability/latency needs. Experienced with production orchestration and operations using Kubernetes and Apache Airflow (DAG-based ETL, scheduling, monitoring/alerts), and has partnered closely with customer service stakeholders to align chatbot behavior with brand voice through iterative testing.”

“Applied NLP/ML engineer with experience at KeyBank and Novartis building production document intelligence and entity-resolution systems in finance and healthcare. Has delivered end-to-end pipelines (Airflow + AWS) using transformers (DistilBERT/Sentence-BERT), vector search (FAISS/Milvus/Pinecone), and human-in-the-loop labeling to achieve measurable gains (40%+ faster queries; up to 88% F1 and 93% precision/90% recall in entity linking).”

“Backend/data engineer with hands-on production experience building FastAPI APIs secured with JWT and delivering AWS-based data processing solutions using Lambda and ECS Fargate. Has worked with Snowflake/third-party API sources and targets including DynamoDB, S3, RDS, Redshift, and Glue, and uses CloudWatch/X-Ray for monitoring and troubleshooting. Seeking ~$65/hr and is open to onsite work in Bethesda, MD.”

“Game economy/progression designer with live-ops experience across MMO, CCG, and F2P strategy titles (ROSE Online, Vega Conflict/KIXEYE). Known for data-driven tuning of loot, currencies, and monetization—e.g., removing a low-performing pay gate drove ~14% DAU lift and improved revenue, and built premium-currency models using real player telemetry to support an ad-to-IAP transition.”

“GenAI/RAG engineer who has deployed a production policy/regulatory search assistant for a financial client using LangChain + Vertex AI, FastAPI, Docker/Kubernetes, and Airflow-orchestrated data pipelines. Demonstrated measurable impact with 50–60% latency reduction and 70% fewer pipeline failures, plus KPI-driven grounding evaluation (90%+ target) and strong cross-functional collaboration with compliance/business teams.”