Vetted SQL Professionals

“Software engineer who owned end-to-end delivery of customer-facing agricultural forecast reporting (crop yield/health) and iterated quickly via rigorous edge-case testing and customer feedback. Also built an internal ML training platform (TypeScript/React + Flask/Python + MongoDB) used by every developer, with architecture designed to stay responsive under heavy compute load.”

“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.”

“Robotics software developer building an ESP32/FreeRTOS wheel-encoder driver for a differential-drive robot with PID velocity control and ROS2 odometry publishing. Has implemented vision-based human tracking (DepthAI) and an obstacle map + EKF sensor fusion using IMU and LiDAR, with a simulation-first debugging workflow (Gazebo, Foxglove) and Dockerized environments; plans to extend to Nav2 + SLAM Toolbox for building-scale mapping.”

“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.”

“Worked in a startup building an automated carbon accounting/climate reporting product, partnering with client IT and internal cross-functional teams to ship features and train end users. Also has software engineering internship experience debugging complex multi-workflow systems, including uncovering a significant (~20%) data annotation error by instrumenting and testing each workflow step.”

“Design engineer blending UX/product design with hands-on technical implementation and analytics. Built a full-stack restaurant web app (Node.js/MongoDB) and improved conversion via instrumentation (sensors/heatmaps) and UX changes; also designed a smart-meter anomaly triage dashboard using Python + Power BI to make complex data usable for non-analysts. Led trust-focused conversational UI direction for an AZ water chatbot based on deep user interviews.”

“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.”

“Worked at easybee ai building a production-grade "voice of the customer" LLM intake agent, hardening a fragile sandbox prototype with JSON-schema constrained outputs, Python/FastAPI validation middleware, and automated retries. Strong in real-time debugging of agentic workflows (snapshot isolation, modular tracing) and in implementing safety/compliance guardrails like a content-moderation middleware to support enterprise adoption.”

“Backend engineer who built a containerized Flask service powering an engineering metrics dashboard by syncing GitHub and Jira data into PostgreSQL, with strong emphasis on schema design, query performance, caching, and background processing. Has hands-on experience with SaaS multi-tenancy (tenant scoping + Postgres RLS) and integrating AI/ML inference via separate model-serving services (FastAPI + TensorFlow Serving) and external APIs (OpenAI/Hugging Face/PyTorch).”

“Platform-minded JavaScript/TypeScript engineer who has maintained and evolved shared NPM-distributed libraries at Capital One and HCA Healthcare, treating internal packages like open source (issue triage, PR reviews, roadmap, docs). Known for methodical debugging and performance work—e.g., diagnosing latency spikes via load testing/instrumentation and redesigning middleware to avoid redundant parsing—paired with strong developer enablement through examples and migration notes.”

“Growth creative marketer who led end-to-end experimentation for Kahana’s Oasis agentic browser launch, repositioning it as a task-specific “productivity multiplier” and validating the message via structured A/B tests across Meta, LinkedIn, and landing pages. Reported performance lift included CPA reductions (23% Meta, 17% LinkedIn) and a 28% ROAS increase, with a repeatable modular framework for rapid creative iteration and hands-on direction of UGC creators and editors.”

“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.”

“Maintained and improved a set of JavaScript packages at JPMorgan, including a major refactor of an event-emission layer used in LLM streaming that delivered ~20% runtime speedup with no API changes. Known for a measurement-driven performance approach (profiling + simplification + validation), strong backward-compatibility discipline, and documentation that accelerates internal adoption and reduces support questions.”

“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.”

“ML/GenAI engineer with recent CVS Health experience building a production RAG system over unstructured financial/research documents using LangChain, FAISS, and Pinecone, plus LoRA/PEFT fine-tuning of GPT/LLaMA for domain-aware summarization. Demonstrates strong applied MLOps and data engineering skills (Airflow/Prefect, Docker/Kubernetes, CI/CD, MLflow) and measurable impact (sub-second retrieval, ~40% better context retrieval, ~25% entity matching improvement).”

“Software engineer with hands-on AWS operations experience who owned an end-to-end manufacturing image ingestion pipeline (on-prem to AWS S3) integrated with MES/WMS. In an early-stage SaaS internship, diagnosed a load bottleneck using K6/New Relic and shipped an NGINX least-connection load-balancing solution that scaled to ~4000 RPS while reducing latency. Also improved maintainability and performance in a React/Node e-commerce codebase, cutting page load time from ~10s to 2.8s.”

“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%+.”

“QA automation engineer with end-to-end ownership of a loan-processing automation suite spanning UI, API, and database validations (Selenium/Playwright/TestNG/REST Assured; Java/Python). Caught and prevented high-impact financial defects (e.g., risk-calculation rounding errors) through CI-driven nightly regressions and API-to-DB checks, and has implemented maintainable Cypress patterns with flake reduction plus GitLab CI gating and Allure reporting.”

“Full-stack engineer who shipped a group scheduling SaaS feature with live availability updates using Next.js App Router + TypeScript, owning production reliability after launch (auth debugging, monitoring, polling/backoff tuning). Has hands-on experience with Postgres schema/index design and query optimization (EXPLAIN ANALYZE) and building durable orchestrated backend workflows with retries and idempotency.”

“Backend-leaning product engineer with DevSecOps depth who has shipped real-time, Kafka-driven data pipelines and AI-enabled customer-facing features to production on AWS. Built a Spring Boot API layer serving real-time predictions at 100K+ requests/day, improving latency by 35% and user task completion by ~25%, and delivered a React/TypeScript dashboard plus a Postgres audit/history model optimized for search and large event volumes.”

“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.”

“Data engineer focused on reliable, scalable analytics pipelines and external data collection. Has owned end-to-end pipelines processing 5–10M records/day, serving Snowflake data marts to Power BI/Tableau, and reports ~99% reliability through strong validation/monitoring. Also shipped versioned REST APIs for curated data with query optimization and caching.”