Vetted Prometheus Professionals

“Built and launched a verified listings system for Burrow (student subleasing) after interviewing ~50 students about scam/fake listing concerns; chose a lightweight .edu-based verification approach to ship fast and then iterated with badges and clearer details, reducing churn from 15% to 7%. Also ran an LLM A/B test for auto-generating listing descriptions and improved trust/accuracy by updating prompts to prevent hallucinated details.”

“DevOps/Infrastructure engineer with hands-on production experience building Jenkins CI/CD pipelines that provision Kubernetes infrastructure and process data into a MapR cluster. Uses Terraform to provision AWS resources (EC2, S3, VPC, subnets) with remote state in S3, separate environment state files, and code review/validation practices; targeting $135k base.”

“Software engineer with JPMorgan Chase experience delivering end-to-end fintech features (Next.js/React/Node/Postgres on AWS) and measurable performance gains. Built and productionized an AI-native credit decisioning workflow combining LLMs, vector retrieval, and a rules engine with strong governance (bias checks, auditability, human-in-loop), improving precision and cutting underwriting turnaround time by 40%.”

“Full-stack engineer in an early-stage startup who built an EV charger monitoring and payments dashboard from scratch, owning UI/UX (Figma), React frontend, Node/Postgres APIs, and production deployment/ops (Firebase + AWS). Demonstrated measurable impact (40% fewer reconciliation errors) and strong reliability chops through multi-source energy/payment ingestion, idempotent pipelines, and CloudWatch-driven incident resolution.”

“Backend/data engineer focused on production Python and AWS: built FastAPI REST services and a containerized ECS Fargate + Lambda architecture deployed via Terraform/CI-CD. Strong in data engineering (Glue/S3/Parquet/RDS) and operational reliability (CloudWatch/SNS, retries, schema-evolution handling), with experience modernizing legacy SAS reporting into Python microservices using feature flags and parity validation.”

“Built and productionized an LLM-powered financial intelligence and forecasting platform at Northern Trust using a RAG architecture (LangChain + Hugging Face + FAISS) with end-to-end MLOps (Docker/Kubernetes, Airflow, MLflow). Emphasized regulatory-grade explainability (SHAP/Power BI) and hallucination control (retrieval-only grounding), achieving ~30% forecasting accuracy improvement and ~65% reduction in analyst research time, with sub-second inference and 95% uptime on EKS/AKS.”

“LLM/RAG practitioner building clinician-facing AI search and Q&A inside EHR workflows, focused on trust, latency, and safety (grounded answers with citations, PHI controls, encryption/audit logs). Demonstrated real-time incident response for production LLM systems (e.g., fixing a metadata-filter deployment regression to prevent irrelevant results/cross-patient leakage) and strong demo/enablement skills for mixed technical and clinical stakeholders; also shipped a multi-model RAG tool at OrbeX Labs with upload/search/audit features for day-to-day adoption.”

“Software engineer with hands-on experience building a decentralized file-sharing dApp, bridging a React frontend with Ethereum smart contracts via Web3.js and integrating IPFS for decentralized storage. Demonstrates a rigorous, measurement-driven approach to performance optimization (profiling + benchmark/regression loop) and strong ownership in high-stakes environments, including Fircosoft sanctions platform optimization and rapid production hotfixes for user-impacting issues.”

“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.”

“JavaScript/React engineer with hands-on open-source library contribution experience, including thoughtful PRs that improved error handling, API flexibility, and added features backed by tests and documentation. Demonstrates a profiling-first approach to UI/runtime performance (memoization, component splitting, render-path optimization) and strong community support skills—reproducing edge cases, delivering sustainable fixes, and communicating workarounds and releases.”

“Full-stack developer with strong cloud/on-prem focus (AWS, VPC networking) who has improved production reliability by bringing manually created IAM/security group resources under Terraform and standardizing environments. Demonstrated end-to-end troubleshooting across app + infrastructure + networking (traffic capture revealed proxy response truncation) and delivered Python-based monitoring/reporting enhancements that improved ops visibility and turnaround.”

“Built and shipped a production agentic LLM analytics platform that lets non-SQL business users query relational databases in plain English via a RAG + LangChain/LangGraph workflow and FastAPI service. Emphasizes safety and reliability with guardrails (validation/access control), testing/evaluation frameworks, and performance optimization (caching, monitoring, Dockerized scalable deployment), reducing dependency on data teams and speeding analytics turnaround.”

“Product-focused full-stack engineer (70% application-layer) who has shipped multi-tenant RBAC for a formerly single-tenant platform, cutting infrastructure costs by 50%. Built high-impact customer-facing features including analytics dashboards (40% retention lift) and a React/TypeScript scheduling grid that reduced navigation time by 60% and setup time by 80%, with solid AWS operations and Postgres performance tuning experience.”

“Robotics/AV perception engineer who built a semantic-segmentation road detection system and integrated it into a ROS-based real-time pipeline (ROS bag camera feed to live monitor) achieving ~12 FPS. Strong in practical deployment work: solved multi-library versioning issues (ROS/OpenCV/TensorFlow), containerized the stack with Docker, and optimized inference by shifting runtime to C++ for large latency gains on NVIDIA hardware.”

“Software engineer who has owned and improved a customer-facing quote flow in a Vue/Nuxt app, using production observability to reduce latency and improve reliability via caching and request-handling fixes. Also shipped an internal LLM Q&A tool using embeddings + RAG over approved company docs and past support tickets, with guardrails, logging, and an evaluation loop that drove retrieval/prompt improvements. Seeking ~$110k base and requires H1B transfer sponsorship.”

“Full-stack engineer in healthcare and enterprise analytics who has shipped event-driven, near-real-time systems (Spring Boot microservices + Kafka + AWS) and large-scale patient/provider portals (50k+ users). Strong in production reliability and performance—measurably reduced claims latency (27%), cut support tickets (25%), and handled real AWS scaling incidents end-to-end. Also built a Python REST control plane for SDN routing integrated with external reinforcement learning agents.”

“Built and shipped a production RAG assistant using GPT-4, LangChain, and Pinecone/FAISS to search 50K+ institutional documents, with a strong focus on groundedness and hallucination reduction through retrieval optimization and re-ranking. Pairs this with a metrics-driven evaluation/monitoring approach (BLEU/ROUGE, manual sampling, logging) and workflow automation via Airflow, and has experience translating stakeholder needs into iterative AI prototypes.”

“Founding engineer who built a multi-tenant AWS backend from scratch focused on ultra-fast, configuration-driven client onboarding and low operational cost. Automated tenant provisioning/deployments with Terraform + GitHub Actions (new client infra in ~13 minutes) and scaled to 62 production clients handling ~75k requests/day without a major rewrite. Hands-on with migrations (DynamoDB->MongoDB), reliability/observability, and performance tuning (indexes, Redis, queueing, connection management).”

“Backend engineer with experience at DigitalOcean and BNY Mellon, specializing in secure, highly available authentication and API platforms. Built an enterprise SSO system integrating Okta via OIDC with resilience patterns (gRPC contracts, circuit breakers, Kafka) and strong encryption, and led a careful monolith-to-Golang microservices migration using shadow traffic, dual writes, and feature flags to preserve data integrity.”

“Backend/data engineer with production experience building real-time sensor telemetry platforms: FastAPI + PostgreSQL services with strong observability, plus AWS serverless and Glue-based ETL into Redshift. Has modernized legacy SAS pipelines into Python microservices and delivered measurable performance wins (Postgres query latency cut to <1 minute and ~60% DB CPU reduction) while owning incident response and reliability improvements.”

“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.”

“Backend engineer with hands-on experience scaling a CVE processing platform by re-architecting it into a Kafka-based distributed system, boosting throughput to 200k+ records/min while designing for HA, deduplication, and fault tolerance. Also led a Flyway-driven migration affecting 15M+ records with staged dev→stage→prod rollout, and has implemented production security patterns (Auth0, OAuth2/HTTPS, AWS IAM RBAC) including least-privilege hardening.”

“AI engineer at Cleveland Clinic building production LLM/NLP systems for radiology documentation, focused on HIPAA-aware, real-time performance across ~298 campuses. Re-architected infrastructure with AWS event-driven services to handle scaling and improved SLA compliance ~40%, and complements this with a personal multi-agent debate system (CrewAI) using local Llama/Mistral plus rigorous evaluation (A/B tests, red teaming, observability).”

“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.”