Vetted Predictive Analytics Professionals

“AI engineer with production experience building a LangGraph-based, stateful multi-agent system at MetLife to automate complex insurance claims adjudication, integrating document discovery, Azure Document Intelligence OCR/extraction, and health data analysis. Strong in agent orchestration and production deployment (Docker + FastAPI REST APIs), with a structured approach to reliability, evaluation, and stakeholder-driven requirements.”

“Built an AI-powered loan automation dashboard using React and open-source JavaScript libraries, with hands-on experience improving real-world performance by reducing re-renders and optimizing/caching multiple API calls. Also produced developer-friendly API documentation for a voice assistant project, helping teammates integrate features faster with fewer errors.”

“Product-focused full-stack engineer (Spring Boot/Django + React/TypeScript) with deep experience building multi-tenant, enterprise workflow and supply-chain/order-tracking systems. Owned an end-to-end Workflow SLA Breach Prediction & Alerting feature integrating Azure ML for a cloud workflow platform used by ~10,000 enterprise users, and has hands-on AWS operations experience resolving real production latency/scaling incidents via query optimization and Redis caching.”

“Healthcare AI/ML engineer with Epic experience who built and deployed a HIPAA-compliant GPT-4 RAG clinical assistant over large medical document sets, emphasizing privacy controls and low-latency performance. Also automated end-to-end retraining and deployment of patient risk models using orchestration/CI-CD (Jenkins, SageMaker, MLflow), cutting deployment time from hours to minutes while improving reliability.”

“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.”

“Software/ML engineer who built a production automated job-finding and cold-email personalization system for Fortune 500 outreach, using JobSpy for dynamic scraping, LangChain orchestration, and LLM+vector DB semantic search with grounding/relevance metrics and guardrails. Also delivered a predictive investment analytics platform for financial advisors, communicating results via Tableau dashboards and portfolio KPIs like Sharpe ratio and drawdowns.”

“Built and productionized an LLM/RAG question-answering service over technical documentation, focusing on retrieval quality (reranking + IR metrics), latency, and scaling. Experienced orchestrating end-to-end ETL/ML workflows with Airflow/Prefect/AWS Step Functions and improving reliability via parallelism, retries, and shadow testing. Also delivered an explainable healthcare risk-flagging classifier with a stakeholder-friendly dashboard for a non-technical program manager.”

“Has worked at a startup within an incubator program and is a certified mentor with a startup mentorship non-profit focused on early-stage/pre-funded companies. Not currently seeking to found a company or take equity-only roles, but open to working at very early-stage companies (pre-launch/pre-revenue/pre-funded).”

“Backend engineer who built an AI-driven "Smart Feedback Analyzer" API (Flask → FastAPI) that processes user feedback with NLP (Hugging Face + OpenAI) and returns structured insights. Demonstrates strong production-minded architecture: stateless services, Cloud Run + Docker deployment, Redis/Celery background processing, and Postgres/SQLAlchemy performance tuning (EXPLAIN ANALYZE, indexing, N+1 fixes), plus multi-tenant data isolation via JWT/API-key derived tenant IDs.”

“Built and deployed a domain-specific LLM chatbot for research/support, cutting manual effort by ~50%. Demonstrates strong applied LLM engineering: RAG, prompt grounding with citations and fallbacks, embedding/top-k tuning, and production monitoring (confidence, latency, feedback loops). Experienced orchestrating agent workflows with LangChain-style pipelines and continuous evaluation to maintain reliability.”

“Built and deployed an enterprise RAG application at Centene to help clinical teams retrieve insights from large internal policy document sets, cutting manual research by 30–40%. Implemented custom domain-adapted embeddings (SageMaker + BERT transfer learning) and hybrid retrieval (BM25 + Pinecone) to drive a 22% relevance lift, and ran the system in production on AWS EKS with CI/CD, MLflow, and Prometheus monitoring (99% uptime, ~40% latency reduction).”

“Python backend engineer who owned an end-to-end Django/DRF authentication and account-management module (JWT, RBAC, email verification) and optimized token validation performance. Has hands-on Kubernetes + Helm delivery with GitOps via ArgoCD (multi-environment app-of-apps, drift detection/rollback) and has supported a cloud-to-on-prem migration using staged testing and phased cutover. Also built and scaled a Kafka-based real-time user activity tracking pipeline with reliability and backpressure controls.”

“Experience spans college-built AWS-hosted Python/Flask web apps and enterprise data work at General Motors, including PostgreSQL query optimization on millions of records and multi-tenant-style data isolation using group-based, column-level permission grants. Also built an AWS-hosted meat price prediction dashboard using Dash/Plotly and ran large nightly data pipelines orchestrated with Apache Airflow.”

“Software engineer with healthcare domain experience (patient monitoring and provider systems) who improves reliability and performance in complex React/Flask applications. Led API standardization for shared internal React utilities using an RFC + deprecation strategy, and optimized a live WebSocket dashboard to handle 3000+ concurrent clinics while reducing client CPU usage. Strong in production debugging, data ingestion validation, and operational improvements like structured logging and alerting.”

“Pharma marketing and go-to-market professional with ~8 years building launch strategies, including experience on the Lovenox marketing team during a major hospital-to-outpatient injectable launch. Demonstrates strong reimbursement expertise (J-code strategy) and hands-on field research with pharmacists across states to enable outpatient reimbursement adoption.”

“Entrepreneurial product builder who created chaintrigger.com in response to early Web3 demand for real-time on-chain event reactions, offering an alternative to The Graph Protocol and achieving adoption among games and other Web3 projects. Currently developing new tools, dogfooding internally, and building distribution via personal network and a niche Twitter/X following to gather feedback and iterate quickly.”

“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.”

“Built and productionized a RAG-based LLM research assistant for biomedical and regulatory document search using Mixtral 7B on SageMaker, LangChain, and Milvus, cutting research time by ~40%. Has hands-on multi-cloud MLOps experience across AWS/Azure/GCP with Kubeflow/Airflow/Composer plus Terraform + ArgoCD, and applies rigorous evaluation/monitoring (latency, accuracy, hallucinations). Also partnered with a non-technical PM to deliver an insurance policy Q&A chatbot that reduced customer response time by 30%+.”

“Founder building AdaptiveWorX™ Flow, a graph-native, agent-first operating system intended to replace document-driven/Agile-era workflows with continuously verifiable, causally linked delivery. Brings decades of systems/architecture perspective plus prior digital health experience, including a PoC turning behavioral therapy sessions into structured longitudinal data using real-time A/V capture and multimodal AI, with an emphasis on compliance, traceability, and scalable platform design.”