Vetted Statistical Analysis Professionals

“Cybersecurity/SOC-focused engineer with hands-on production experience integrating and tuning Splunk Enterprise Security for a zero-trust program, including CIM normalization, correlation/risk-based detection tuning, and performance optimization via forwarder-level filtering and index strategy. Has cross-disciplinary incident troubleshooting experience spanning SIEM, networking, and hardware, and has automated customer/team-specific security workflows using Python in Splunk playbooks; collaborated on-site with HSBC IT/SOC teams to deliver dashboards and security policies.”

“ML/AI practitioner in healthcare (Syneos Health) who has deployed production clinical NLP and risk models. Built a BERT-based physician-note information extraction system on Docker + AWS SageMaker (reported ~42% retrieval improvement) and automated retraining/deployment with Airflow and drift detection, while partnering closely with clinicians to drive adoption (reported ~18% readmission reduction).”

“Built and deployed a GPT-4-powered medical assistant for clinical staff to reduce time spent searching guidelines and EHR information, with a strong emphasis on safety and compliance. Uses strict RAG, confidence thresholds, and fallback behaviors to prevent hallucinations, and runs production-grade workflows orchestrated with LangChain/LangGraph plus Docker/Kubernetes/MLflow and monitoring for reliability and cost.”

“LLM/ML engineer who has shipped an enterprise RAG-based Q&A system (LangChain/LlamaIndex, FAISS + Azure Cognitive Search, GPT-3.5/4 via OpenAI/Azure OpenAI) to production on Docker + Kubernetes/OpenShift, tackling hallucinations, retrieval quality, latency/cost, and RBAC/IAM security. Also partnered with operations leaders to turn manual reporting into an LLM-powered summarization and forecasting dashboard driven by real KPIs and iterative stakeholder feedback.”

“Built and deployed a production LLM-powered document Q&A system enabling natural-language querying of large PDFs, focusing on retrieval quality (overlapped chunking) and low-latency performance (optimized embeddings + vector search). Experienced with scaling ML/LLM workflows using async/batch processing, caching, cloud storage, and orchestration via Apache Airflow with robust testing, monitoring, and failure handling.”

“Applied NLP/ML in healthcare and financial services, including fine-tuning BERT on unstructured EHR text and building embedding-based similarity search for clinical concepts. Also redesigned a Wells Fargo fraud detection data pipeline using modular Python + AWS Glue/Step Functions, cutting runtime ~40% with improved monitoring and reliability.”

“Backend engineer focused on productionizing LLM systems: built a FastAPI-based RAG and multi-agent automation platform deployed with Docker/Kubernetes, prioritizing safe execution and reduced hallucinations. Experienced in refactoring monolithic ML services with feature-flagged incremental rollouts, and implementing JWT/RBAC plus row-level security (e.g., Supabase) for secure, scalable APIs.”

“AI/ML engineer focused on real-time, production-grade LLM systems, with a robotics-adjacent mindset around latency/accuracy tradeoffs and modular pipelines. Built a scalable RAG-based assistant orchestrated as microservices on Kubernetes with Kafka async messaging, ONNX/quantization optimizations, and monitoring (Prometheus/Grafana), citing a ~35% hallucination reduction; has also experimented with ROS Noetic/Gazebo to understand ROS concepts.”

“Machine Learning Engineer currently at Webster Bank building an enterprise-scale LLM agent for Temenos Journey Manager/Maestro, using RAG-style multi-stage retrieval with FAISS/Pinecone, hybrid dense+sparse search, and LoRA fine-tuning optimized via NDCG/MAP and A/B testing. Previously handled messy incident/telemetry data at Deuta Werke GmbH with deterministic + fuzzy entity resolution, and has strong production data engineering experience across Spark/Hadoop and Python ETL systems.”

“Built and productionized AI-driven insurance claims document intelligence/automation at American Family Insurance, integrating OCR/NLP models and a rules-based validation layer into existing claims systems via APIs. Delivered measurable impact (≈28% accuracy lift, ≈35% reduction in manual processing time) and modernized legacy workflows with phased cloud migration, feature flags, parallel runs, and CloudWatch-based monitoring.”

“LLM workflow/agentic systems practitioner who has helped customers harden an LLM-based incident triage prototype into a trusted daily-use production system by adding observability, audits, confidence gating, and deterministic fallbacks. Brings an SRE-style approach to real-time debugging (trace replay, rollback/canary, safe toggles) and is experienced running developer-centric demos/workshops and partnering with sales on technical qualification and security/architecture artifacts.”

“Healthcare-focused LLM engineer who has built two production clinical applications: an automated structured clinical report generator from physician-patient conversations and a RAG-based chatbot for retrieving patient history (procedures, allergies, etc.). Demonstrates strong applied RAG expertise (overlapping chunking, entity dependency graphs, temporal filtering, graph RAG) to reduce hallucinations/omissions and partners closely with clinicians to automate hospital workflows.”

“Healthcare data/ML practitioner with experience at UnitedHealth Group building production ETL and streaming pipelines (Python, BigQuery, Kafka) that unify EHR, IoT device, and lab data for patient risk prediction. Also implemented embedding-based semantic search/linking for noisy clinical notes via domain adaptation and rigorous validation with clinical stakeholders; previously built churn prediction at DirecTV using XGBoost.”

“Backend engineer with experience evolving a high-volume agricultural loan processing platform (APMS) at HDFC Bank, emphasizing transactional integrity, auditability, and modularity while integrating with credit bureaus, document management, and risk engines. Also improved automation/reporting robustness at Trend Micro by catching duplicate-event retry edge cases and adding idempotency safeguards.”

“Customer-facing technical professional with experience supporting LLM/agentic-style workflows and complex integrated systems (APIs, backend logic, databases). Partnered with sales/customer teams at Radix Health to onboard new clients in phased prototypes, translating non-technical requirements into technical scope and implementing core product changes to tailor the appointment-booking solution for providers.”

“Operations/project leader with hands-on experience driving an end-to-end transition of legacy customers from on-prem to cloud, including customer communication, structured execution planning, and operational handover. Also improved field operations productivity by selecting and implementing a field management tool that automated work orders and replaced spreadsheet/email workflows with mobile app-driven data flows.”

“ML/GenAI engineer with production experience building an LLM-powered governance layer that summarizes verified drift/performance signals into validation reports and release notes, designed for regulated environments with de-identification and non-blocking fallbacks. Strong Airflow-based orchestration background across healthcare and finance, integrating Databricks/Spark and MLflow for scalable retraining/monitoring. Demonstrated ability to partner with non-technical healthcare operations teams to deliver actionable risk-scoring outputs via dashboards and automated reporting.”

“Backend/data platform engineer who led a major redesign of a hybrid streaming+batch analytics platform processing 10+ TB/day (Airflow/Hive/BigQuery) with strong data-quality automation. Also built a production RAG PDF assistant with concrete mitigations for hallucinations and prompt injection (re-ranking, grounding, verifier step) and has deep experience executing low-risk migrations (dual-write, blue-green, rapid rollback) and implementing JWT-based row-level security.”