Vetted ETL Professionals

“Built and deployed a production agentic LLM knowledge assistant that answers complex questions over internal documents, APIs, and databases using a RAG architecture (FAISS/Pinecone) and LangChain/LangGraph orchestration. Emphasizes production-grade reliability and hallucination control through grounding, confidence thresholds, validation, retries/fallbacks, and full observability (logging/metrics/traces) with continuous evaluation and feedback loops.”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”

“Backend/AI engineer with experience in both high-scale financial services (JP Morgan trade compliance analytics API on Java/Spring Boot/Postgres/Elasticsearch on AWS EKS processing 1M+ trades/day) and applied LLM systems for legal research (LangChain/OpenAI + Weaviate semantic search). Demonstrated strength in reliability/performance engineering, data consistency during migrations, and production-grade workflow orchestration with observability and human-in-the-loop guardrails.”

“ML/LLM practitioner who has shipped production RAG systems (summarization + Q&A) and end-to-end Airflow-orchestrated demand forecasting pipelines at NEON IT. Strong focus on reliability—uses evaluation scripts, retrieval/chunking tuning, validation/retries/alerts, and stakeholder-driven iteration to make AI workflows consistent and usable.”

“Built a production-oriented multimodal RAG "Fix Assistant" with FastAPI, Tavily search, BM25 + cross-encoder reranking, and a local Phi-3.5 model, emphasizing strict grounding and fallback/verification modes to prevent hallucinations. Also has hands-on federated learning experience using STADLE to orchestrate edge-node training and aggregation for EV telemetry data, plus experience communicating AI results to non-technical stakeholders (traffic RL/congestion outcomes).”

“Backend/ML engineer working on a battery energy storage system operations dashboard: built a Flask backend integrated with OAuth and a separate FastAPI optimization/simulation service, deployed via Docker CI/CD to Azure Container Apps. Strong in productionizing ML (AzureML to batch endpoints) and in performance/scalability patterns (Postgres indexing/JSONB, per-unit data isolation, async throttling + caching for year-long CPU-intensive simulations across 40+ scenarios).”

“Full-stack engineer with Bank of America experience modernizing a large-scale financial reporting platform. Built React frontends and Java/Spring Boot microservice APIs end-to-end, optimized data-heavy SQL performance (indexing/caching/pagination), and implemented an AI feature for forecasting and anomaly detection using Python/scikit-learn, with deployments supported on AWS.”

“Small-team builder of Promethium’s “Mantra” next-gen agentic text-to-SQL engine, using vector DB + LangGraph tooling and SQL validation/evaluation to improve query accuracy. Experienced in diagnosing production LLM workflow failures via LangSmith traces and in running hands-on developer workshops and pre-sales POCs with live debugging and real customer data.”

“Backend/data engineering candidate specializing in Python/Flask services and ML-enabled systems, deploying containerized workloads on AWS ECS/EKS with strong observability (Prometheus/Grafana) and PostgreSQL performance tuning. Built multi-tenant architectures with row- and schema-level isolation and optimized a Kubernetes-based Airflow + Spark nightly ETL pipeline for an e-commerce client, improving performance by 250%+ and reliably beating morning reporting deadlines; also contributed to Apache Airflow (SQLAlchemy/PostgreSQL area).”

“Built production LLM systems including a real-time customer feedback analysis and workflow automation platform using RAG and multi-agent orchestration with confidence-based human escalation, addressing privacy and legacy integration challenges. Also automated ML operations with Airflow/Kubernetes (e.g., daily churn model retraining) cutting retraining time to under 30 minutes, and demonstrates a rigorous testing/monitoring approach plus strong non-technical stakeholder collaboration.”

“Engineering leader with hands-on coding time who has driven major commerce and data-platform transformations: defined goop’s omnichannel strategy, unified payments to Square, and rebuilt real-time NetSuite inventory flows plus forecasting tools. Currently reorganized engineering into Product/Data/Support teams to hit aggressive seasonal roadmaps, and led a data-lake/medallion ELT refactor feeding embedded analytics (Tinybird) with improved reliability and cost efficiency; also accelerates onboarding via AI coding tools in a serverless, event-driven architecture.”

“Built a production "sentinel insight engine" to tame information overload from millions of product reviews and support transcripts, combining Azure OpenAI (GPT-3.5) zero-shot classification with a fine-tuned T5 summarizer to generate weekly actionable product insights. Demonstrated strong MLOps/production engineering by adding drift monitoring with embedding-based detection, integrating REST with legacy SOAP/queue-based CRM via FastAPI middleware, and scaling reliably on Kubernetes with HPA.”

“Healthcare-focused data engineer/ML practitioner with experience at Lightbeam Health Solutions and Humana building production entity-resolution and semantic similarity pipelines across EMR, lab, and claims data. Uses NLP/ML (spaCy, scikit-learn, BioBERT/LightGBM) plus Snowflake/Airflow and vector search (Pinecone) to improve linkage accuracy (reported 90%) and semantic match quality (reported +12–15%), while reducing manual cleanup by 40%+.”

“Providing technical guidance to a small team exploring a biomedical startup focused on earlier disease detection, including for remote/underserved areas. The venture is in ideation with initial research completed and is moving toward prototyping while exploring initial investment/support.”

“AI/data engineer who built a production LLM-powered schema drift detection system (LangChain/LangGraph) to catch semantic data changes before they break downstream analytics/ML. Deployed on AWS with Docker/S3 and implemented an LLM-as-a-judge evaluation framework to improve trust, reduce hallucinations, and control false positives/alert fatigue. Collaborated with non-technical risk/business analytics stakeholders at EY by delivering human-readable drift explanations that improved confidence in financial analytics dashboards.”

“Cloud/Kubernetes-focused production engineer with experience running 99.95% uptime platforms across AWS/Azure/GCP. Strong in incident response and performance troubleshooting (including a 30% MTTR reduction), and in building secure CI/CD and Terraform-based IaC for AKS/GKE microservices with robust change controls and rollback practices. Notably does not have direct IBM Power/AIX/VIOS/HMC or PowerHA/HACMP ownership.”

“Data engineer with end-to-end ownership of large-scale retail and clinical data ingestion/processing on AWS, including real-time streaming and batch pipelines. Delivered measurable outcomes: 20M daily transactions processed, latency cut from 4 hours to 5 minutes, ~70% fewer failures, and 120+ pipelines running at 99.8% reliability with full audit compliance.”

“GenAI/LLM ML engineer (currently at Webprobo) building an enterprise GenAI platform with document intelligence and automation on AWS and blockchain. Has hands-on experience with RAG, LLM evaluation tooling, and orchestrating production LLM workflows with Apache Airflow, plus deep exposure to reliability challenges in globally distributed/edge deployments. Also partnered with business/marketing stakeholders at a banking client to deliver an AI-driven customer retention insights solution.”

“Built and deployed a compliance-sensitive LLM pipeline that extracts rebate logic from hospital–supplier medical contracts, using multi-layer redaction (regex/NER/dictionary), schema-validated structured outputs, and secure placeholder reinsertion. Hosted models on Amazon Bedrock to avoid retraining on sensitive data and improved both accuracy and cost by splitting the workflow into a lightweight section classifier plus a fine-tuned extraction model, orchestrated with LangChain and evaluated via layered, test-driven agent assessments.”

“Backend/ML-leaning engineer who built a content-based event recommender for FlowMingle using embeddings + HNSW vector search on Google Cloud, with Firebase as the backend and a managed recommendation lifecycle (15 recs/user, daily async generation, weekly deletion) now serving 1500+ users. Also led a cost-driven migration of ConvAI services to Azure AI using parallel request testing from a Unity client, with post-migration monitoring via logs and model evals; contributed to a Massachusetts law-enforcement conversation analysis system by expanding ingestion to PDF/TXT/Excel and multi-file inputs.”

“Backend/data engineer who builds production FastAPI microservices and AWS serverless/Glue pipelines for SMS analytics and marketing segmentation. Led a legacy batch modernization into modular services (FastAPI + Glue/Athena + ClickHouse) using shadow-mode parity checks, feature flags, and incremental rollout. Demonstrated measurable performance wins (12s to sub-second SQL; ~40% CPU reduction) and strong incident ownership with proactive schema-drift prevention.”

“LLM/agent engineer who built a production LangGraph multi-agent orchestrator connecting GitHub and APM/observability signals with a chain-of-verification loop for root-cause analysis. Emphasizes pragmatic architecture (start simple with state summaries), performance tuning (async LLM calls, Docker), and rigorous evaluation (LLM-as-judge, adversarial testing, hallucination/instruction adherence metrics, tool-call tracing) while iterating with non-technical stakeholders via A/B testing.”

“Built and deployed a production LLM-powered calorie-counting chatbot that turns plain-English meal descriptions into normalized food entities, quantities, and calorie estimates using a hybrid transformer + rule-engine pipeline. Emphasizes reliability with schema/constraint guardrails, confidence-based routing (including embedding similarity search fallbacks), and strong observability/metrics (hallucination rate, calibration, latency, cost). Partnered closely with nutritionists to encode domain standards into mappings and validation logic.”