Vetted Data Quality Professionals

“Built and deployed LLM-powered analytics and reporting systems, including a RAG-based assistant over Snowflake that let business users ask questions in plain English instead of writing SQL. Experienced orchestrating LLM agents (LangChain) and serverless reporting pipelines (AWS Lambda/S3/RDS), with a strong focus on grounded outputs, monitoring/evaluation, and data quality—used daily by non-technical finance and operations teams at Cigna.”

“TSE with hands-on experience troubleshooting customer-reported data issues across APIs and SQL, coordinating with engineering on hotfixes, and translating risk to non-technical stakeholders. Has supported application security workflows using Veracode by generating reports, driving remediation via Jira, and tracking exposure metrics; also assisted customers with SSO setup (client ID/secret).”

“Backend/data engineer with experience at Assurant and Capgemini, focused on reliability and performance at scale. Improved high-latency backend APIs by adding and iterating on a Redis caching layer driven by CloudWatch/monitoring metrics, and built scalable BI pipelines that normalize messy multi-source enterprise data with strong observability and error handling. Familiar with LLM/RAG architecture and practical guardrails, though has not yet shipped an LLM feature to production.”

“Backend/data engineer with production experience building Python FastAPI services and AWS-native data pipelines. Has delivered containerized and serverless workloads (ECS/EKS/Lambda) with Terraform-based IaC, strong reliability patterns (JWT/RBAC, retries/circuit breakers, observability), and AWS Glue ETL into S3/Redshift. Demonstrated measurable SQL performance wins (40–50s to <4s) and owned real pipeline incidents through detection, mitigation, and prevention.”

“ML/LLM engineer with Meta experience building production AI systems for near real-time user-report classification and summarization under strict latency (<250ms), safety, cost, and privacy constraints. Has hands-on MLOps/orchestration experience (Airflow, Spark, MLflow, Kubernetes, Docker, GitHub Actions) plus observability (Prometheus/Grafana) and applies rigorous evaluation, staged rollouts, and A/B testing to keep agent workflows reliable in production.”

“Backend/data engineer who builds Python (FastAPI) data-processing API services for internal analytics/reporting, emphasizing modular architecture, async performance tuning, and reliability patterns (health checks, retries, observability). Also migrated legacy on-prem ETL pipelines to Azure using ADF/Data Lake/Functions and implemented a near-real-time ingestion flow with Event Hubs plus watermarking to handle late events and deduplication.”

“LLM engineer/data engineer who has deployed production RAG systems for internal-document Q&A, building end-to-end ingestion, embedding, vector search, and FastAPI serving while actively reducing hallucinations and latency through rigorous retrieval tuning and caching. Also experienced in orchestrating cloud data pipelines (Airflow, AWS Glue, Azure Data Factory) and partnering with non-technical business teams to deliver AI solutions like automated document review.”

“Marketing leader who has repeatedly been the first marketing hire, building the function from scratch while aligning sales, product, and leadership around shared positioning and goals. Strong in marketing ops and executive analytics—integrated multiple data platforms to track the full customer lifecycle (CLV, renewals, attribution) and drive KPI-focused decision-making while reducing reliance on vanity metrics.”

“AI/ML practitioner in healthcare/insurance (Blue Cross Blue Shield) who built and deployed a production NLP system to classify patient risk from unstructured clinical notes. Experienced in end-to-end pipeline orchestration (Airflow, AWS Step Functions/Lambda/SageMaker) and real-time optimization (BERT to DistilBERT on AWS GPUs), with strong clinician collaboration to drive adoption.”

“Worked on taking an LLM-based system at Soundmakr from prototype to production by adding prompt constraints, validation/guardrails, deterministic ranking, and robust logging/monitoring with feedback loops. Also partnered with product/marketing during an internship on Thea: Study Smart to analyze onboarding drop-offs and run A/B tests on AI-driven flows, translating results into actions that improved retention and conversion.”

“Data/Backend engineer who has owned end-to-end merchant analytics systems on AWS: orchestrated multi-source ingestion (FISERV/Shopify/Clover) with Step Functions/Lambda, enforced strong data quality gates, and served curated datasets via Redshift and a FastAPI layer. Also built an early-stage Merchant Insights AI agent that converts natural language questions into SQL using OpenAI models, with full CI/CD and observability.”

“ML/NLP engineer with healthcare and industrial IoT experience: built an Optum pipeline that converted 2M+ physician notes into structured entities and linked them with claims/pharmacy data to create an actionable patient timeline. Deep hands-on expertise in production NER, entity resolution, and hybrid search (Elasticsearch + embeddings/FAISS), plus robust data engineering practices (Airflow, Spark, data contracts, auditability) and experimentation-to-production rollout via shadow mode and feature flags.”

“Worked on large financial analytics datasets combining complaint text, transaction logs, and demographics; built end-to-end NLP/ML pipelines (TF-IDF + Random Forest) and data integration in BigQuery with Tableau reporting, citing ~95–98% accuracy. Also implemented entity resolution with fuzzy matching and semantic linking using BERT sentence-transformer embeddings stored in FAISS, including fine-tuning on labeled pairs to improve search/linking relevance.”

“Healthcare data/NLP practitioner with experience at UnitedHealthcare building production ML systems that connect unstructured call center transcripts and medical notes to structured claims data. Has delivered measurable impact (25% classification accuracy lift; ~30% relevance improvement) using classical NLP, embeddings (Sentence-BERT + FAISS), and AWS SageMaker deployments with robust validation and drift monitoring.”

“Engineering leader with a strong automation-first philosophy ("special treatment doesn't scale"), experienced in building self-service tooling and communicating clearly with executives via BLUF-style updates. Has delivered end-to-end business-driven solutions—from sourcing alternative vendor data to installing infrastructure and writing drivers/analytics—and led pragmatic architecture changes in R/Rserve that significantly improved performance while driving cloud costs toward near-zero.”

“Built Hexagon’s production Text-to-CAD Copilot that converts text and rough sketches into editable CAD code, combining GraphRAG (Neo4j/LangChain) with a Gemini-powered vision module and multi-agent geometric validation—cutting manual modeling from a day to ~45 seconds and driving retrieval latency below 50ms. Also has large-scale GCP data/ML orchestration experience (Airflow/Cloud Composer, Dataflow, Pub/Sub, Snowflake) processing 50M+ daily records with drift monitoring and automated reliability controls.”

“Built and productionized an LLM-based financial document analysis system using a RAG pipeline, including robust ingestion/chunking/embedding workflows, vector DB retrieval, and an AWS-deployed FastAPI service containerized with Docker. Demonstrates strong applied expertise in improving retrieval quality and latency at scale, plus hands-on experience debugging agentic/LLM workflows with monitoring and trace-based analysis while supporting demos and customer-facing adoption.”

“Data engineer with healthcare/clinical trial domain experience who owned a 100TB+/month AWS pipeline end-to-end (Glue/S3/Redshift/Airflow) and drove measurable outcomes (20% lower latency, 99.9% reliability, 40% less manual reporting). Also built production data services and API-based ingestion on GCP (Cloud Run/Functions/BigQuery) with strong validation, versioning, and safe migration practices, and launched an early-stage RAG solution (LangChain + GPT-4) for researchers.”

“Data engineer with banking FP&A experience who led an end-to-end migration of 10+ TB from Teradata to Azure (ADF + Data Lake + Databricks/PySpark + Synapse). Emphasizes reliability (multi-stage validation, monitoring/alerts) and performance (Spark tuning, incremental loads, autoscaling), reporting ~99.5% pipeline reliability while supporting downstream consumers with stable schemas and clear change management.”

“Executive/engineering leader with 18+ years at an established company who helped turn a top-3 telco data opportunity into meaningful revenue and a continuing partnership to launch new data-driven products. Brings a security/privacy-first approach (compliance assessments and documentation) and a structured North Star + technical planning method for building with limited resources; interested in joining an early-stage founding journey with the right vision and team.”