Vetted Anomaly Detection Professionals

“Data/ML practitioner with experience spanning medical imaging (retinal vessel analysis for hypertension/CVD risk prediction) and enterprise data engineering at Carl Zeiss. Built large-scale SAP data cleaning/validation pipelines (10M+ daily records, ~99% accuracy) and RAG-based semantic search with LangChain/vector DBs that cut manual querying by 82%, plus automation that reduced data onboarding from 8 hours to 12 minutes.”

“Robotics/IoT engineer who deployed a fog-enabled real-time monitoring system (edge Raspberry Pi + MQTT + cloud logging) and validated it via an IEEE-indexed publication. Strong in autonomous navigation with ROS/Gazebo, SLAM/localization, and cross-layer debugging using timing/transform-delay correlation. Extends Python computer vision pipelines (YOLO + OpenCV/Albumentations) for custom datasets and weather-specific conditions.”

“QA automation engineer with ad-tech domain experience (Prebid.js wrapper-based services) who built an end-to-end Python automation framework using Playwright to validate wrapper settings, auction request/response, and analytics payloads. Uses Jenkins-driven CI reporting and feature-categorized regression runs to quickly isolate revenue-impacting defects and coordinate fast fixes with developers.”

“Full-stack engineer with end-to-end ownership across React/TypeScript frontends, Spring Boot/Node microservices, and production ops on Docker/Kubernetes and AWS (ECS/CloudWatch). Built real-time healthcare eligibility and analytics systems at Cigna and an early-stage seller onboarding platform at Flipkart, driving measurable performance gains (35–40% latency/throughput improvements) through event-driven Kafka pipelines, Redis caching, and strong reliability/observability practices.”

“Built a production multi-agent recommendation/RAG system for internal data analysts to speed up weekly report creation by improving document discovery and automating report/SQL generation. Implemented LangGraph-based orchestration with deterministic agent routing, robust error handling (interrupt/resume), and metadata-driven semantic chunking for diverse PDF/document formats, plus monitoring for latency, throughput, and token/cost efficiency.”

“Built and owned an end-to-end AV operations automation and dashboarding platform for USC event operations, used daily to coordinate hundreds of live events. Delivered a React/TypeScript full-stack system integrating Smartsheet APIs with strong reliability practices (typed contracts, validation/fallbacks, safe rollouts) and experience with queue-based microservice patterns (idempotency, retries, DLQs, monitoring).”

“LLM/agent workflow engineer with healthcare experience (CVS/CBS Health) who built and deployed a production call-insights platform using Azure OpenAI + LangChain/LangGraph, including sentiment and compliance checks. Demonstrates deep HIPAA/PHI handling (tenant-contained processing, redaction, RBAC/encryption/audit logging) and production rigor (testing, eval sets, validation/retries, autoscaling) to scale to thousands of transcripts.”

“Built and deployed a production Retrieval-Augmented Generation (RAG) platform in a healthcare setting to automate clinical documentation review and summarization, targeting near-real-time, explainable outputs. Emphasizes grounded generation to reduce hallucinations, latency optimizations (chunking/embedding reuse), and PHI-safe workflows with access controls, plus strong orchestration experience using Apache Airflow.”

“Built and productionized a telecom-focused RAG assistant by LoRA fine-tuning LLaMA-2 and integrating LangChain+FAISS behind a FastAPI service, with dashboards and a human feedback UI for engineers. Demonstrated measurable impact (≈40% faster document lookup, +8–10% retrieval precision) and strong MLOps rigor via Airflow orchestration, CI/CD, and monitoring for drift and failures.”

“LLM engineer who has deployed production RAG systems for regulated document QA (PDFs/knowledge bases), emphasizing grounded answers with citations, RBAC, monitoring, and continuous feedback. Demonstrates deep practical expertise in retrieval quality (semantic chunking, hybrid BM25+embeddings, re-ranking), reliability (guardrails, deterministic workflows), and measurable evaluation (golden sets, log replay, A/B tests) while partnering closely with compliance/operations stakeholders.”

“AI/ML practitioner with Northern Trust experience who has shipped production LLM systems (internal support assistant) using RAG, vector databases, orchestration (LangChain/custom pipelines), and rigorous monitoring/feedback loops. Also built AI-driven fraud detection/risk monitoring solutions in a regulated financial environment, emphasizing explainability (SHAP), audit readiness, and stakeholder trust through dashboards and clear communication.”

“At JP Morgan Chase, built and deployed a production LLM-powered RAG knowledge assistant to help fraud investigators and risk analysts quickly navigate regulatory updates and internal policies, reducing investigation delays and compliance risk. Strong focus on secure retrieval (RBAC filtering), reliability (layered testing + observability), and production constraints (latency/SLOs), with Airflow-orchestrated, auditable ML pipelines.”

“Built and deployed a production LLM/RAG document automation system for business documents (contracts/claim forms) that extracts schema-validated JSON, generates grounded summaries/Q&A, and integrates into transaction systems via APIs. Emphasizes real-world reliability: hallucination controls, layout-aware parsing with OCR fallback, Step Functions-orchestrated workflows with retries/timeouts, and human-in-the-loop review designed in close partnership with operations and claims stakeholders.”