Vetted Power BI Professionals

“Backend engineer with insurance/claims domain experience who modernized legacy claims processing systems to support AI-assisted claim review. Emphasizes production-ready API design in Python/FastAPI (schemas, async, caching, graceful degradation), strong observability with Prometheus, and layered security including JWT auth plus database row-level security (Supabase/Postgres).”

“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.”

“Growth/brand leader who drives measurable revenue outcomes through rebrands, GTM execution, and multi-channel acquisition. Has delivered on-time/on-budget website + brand rebuilds via sourced expert partners, improved conversion from 3% to 8% for an MSP using outsourced outbound, and helped an industrial manufacturer exceed a $2M revenue goal by reaching $3M through repositioning and market expansion.”

“Built and deployed an LLM-powered mental health therapy assistant at AppHealth that segments users by stress level and delivers personalized, non-medical guidance. Implemented healthcare-focused safety guardrails (secondary LLM output filtering) and a multi-agent router workflow validated via statistical tests and therapist review, then scaled training/inference on AWS (EC2/Lambda/DynamoDB) with Kubernetes.”

“Cloud/ML engineer with production deployment experience on Azure (Dockerized models, managed APIs, data pipelines) who has repeatedly stabilized unreliable systems—e.g., taking an API-driven analytics pipeline from ~60% to 98% reliability and an Azure ML service from ~80% to 97% by addressing rate limits, container memory, and gateway timeouts. Also built an explainable contract-risk model for entertainment bookings (Transformers + SHAP) and integrated it into a legacy booking system via a Flask REST API, plus prior IoT work at Nissan processing CAN bus sensor streams for diagnostics/anomaly insights.”

“Built and deployed a production enterprise LLM-powered RAG assistant for the construction domain, enabling natural-language querying across PDFs/reports and structured sources (SQL/CSV). Implemented an agent-based routing and multi-agent orchestration approach (LangChain/LangGraph) to reduce hallucinations, improve latency, and deliver actionable, structured responses based on stakeholder feedback.”

“Procurement/sourcing professional with end-to-end ownership of vendor selection and cost control, leveraging SAP spend analysis, RFIs/RFQs, and structured supplier capability/quality assessments (ISO, PPM, inspections). Notably streamlined an ordering/invoicing process to reduce blocked invoices and push payments through EDI, and uses Excel/Power BI plus workflow tools (123forms) to manage milestones, approvals, and OTD.”

“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 with co-op experience at Nokia and prior work at Nimble, delivering React/TypeScript single-page onboarding flows and internal web apps. Builds from Figma to production React, emphasizes modular architecture and consistent UI via Material UI, and applies Jest-based unit/integration testing plus lazy loading to improve reliability and performance in both new and existing codebases.”

“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.”

“ML engineer/data scientist who built and deployed a real-time fraud detection platform at Citi on AWS SageMaker, processing 3M+ daily transactions and improving fraud response by 28%. Combines unsupervised anomaly detection (autoencoders) with ensemble models (XGBoost/Random Forest) plus Airflow/Step Functions orchestration, drift monitoring, and explainability (SHAP) to keep models reliable and compliant in production.”