Vetted REST API Professionals

“Built and productionized an AI agent-based in-app assistant at ISAFE to guide users through document workflows, piloting with a partner school district and then rolling out across districts. Combines hands-on LLM/agent debugging (logs, fallback rates, state/context tracking) with strong technical demos and sales enablement through live workflows and pilot programs (e.g., Osceola School District).”

“Built and productionized a tool-first, multi-agent framework that augments an anomaly detection model with domain context to generate trustworthy, evidence-backed anomaly explanations (including false-positive likelihood). Architected the platform to be model/orchestration/vectorDB agnostic (e.g., GPT + CrewAI + ChromaDB vs Claude + LangGraph + other vector DB) with strong performance, reliability, and OpenTelemetry-based observability. Also built a personal LangGraph-based "mock interviewer" agent that asynchronously fuses voice + live code input using state reducers, stop conditions, and fallback routing.”

“Frontend engineer on Ford EV infotainment HMIs building React + TypeScript workflows for Apple CarPlay/Android Auto device projection and high-stakes reset flows. Deep experience integrating a React HMI with legacy Qt expectations and C++ services via MQTT, using Redux Saga/state-machine patterns to manage complex async device and system operations with strong QA and performance optimization practices.”

“AI/ML infrastructure-focused engineer who has built production RAG systems from scratch (Supabase/pgvector + OpenAI embeddings) and iterated using formal eval metrics to improve retrieval quality. Also debugged real-time audio issues in a LiveKit-based pipeline by correlating packet loss with VAD behavior, and has deep experience building brittle, customer-specific financial platform integrations in Python/Playwright (2FA, redirects, token refresh, rate limits).”

“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.”

“Full-stack engineer who built and owned an internal analytics dashboard for sales (React/TypeScript + Node/Express + NoSQL), delivering it two weeks early with zero production issues and a reported 10% sales-efficiency lift. Experienced with microservices and async messaging patterns (retries/DLQs/idempotency), and emphasizes rapid iteration with strong CI/CD and automated testing plus user-driven adoption.”

“Built and productionized a GenAI prompt-engineering solution to retrieve prevailing wages based on job/location selections, emphasizing accuracy through stricter prompt templates and validation. Hands-on in real-time production debugging using Splunk (callback tracing, verbose logging, header inspection) and experienced running developer-facing demos/workshops that helped drive marketplace API adoption.”

“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.”

“Cloud-native integration engineer (Oracle/OCI) with strong production deployment and incident-response experience, including API gateway rollouts, observability (Prometheus/Grafana), and multi-layer debugging for payments systems. Built Python/FastAPI microservices and automation for customer-specific reporting and data sync, and has delivered major performance gains (45 min to <10) plus reliability improvements (MTTD reduced 40%+) through monitoring, playbooks, and resilient integration patterns (streaming/queuing, retries, secure tokens, VPC peering).”

“Software engineer with experience spanning an SEL internship and Walmart, combining backend/data pipeline work (Python, Kafka, relational DBs) with DevOps practices (Docker, Grafana, GitHub/Jenkins CI/CD, GitOps). Notably contributed to a REST-to-GraphQL migration aimed at reducing cloud utilization and implemented testing strategies to validate the transition.”

“LLM/agentic systems practitioner focused on taking customer prototypes to production by hardening reliability (APIs, monitoring, security) and adding guardrails, evals, and incremental rollouts. Experienced diagnosing RAG/agent failures via structured tracing and fixing retrieval-quality issues (freshness checks, filters, schema enforcement). Also supports pre-sales by leading developer demos/workshops and building targeted POCs to address scalability/reliability objections and drive adoption.”

“Built and shipped multiple production LLM/agentic systems, including an agentic RAG NL-to-SQL analytics app that cut manual reporting from 9 hours/week to 15 minutes by grounding on schema-aware retrieval and robust fallback/monitoring. Also implemented a LangChain supervisor-orchestrated enterprise IT automation agent that routes requests for search, identity validation, and action execution, and created a RAG search tool spanning Jira/Confluence/SharePoint for operations stakeholders.”

“Backend/platform engineer (American Express) who built a Flask-based orchestration layer to automate infrastructure provisioning and integrated Azure AD/JWT RBAC security. Strong in PostgreSQL/SQLAlchemy performance optimization (70%+ query-time reduction) and scalable async/event-driven architectures, including ML inference pipelines (SageMaker/Azure ML/Hugging Face) and high-throughput job queues (Celery/Redis) with reliability patterns like DLQs and idempotency.”

“Healthcare-focused applied ML/LLM engineer who has deployed production systems including an LLM medical documentation assistant that summarizes unstructured EHR notes into physician-ready structured outputs. Experienced building secure, compliant pipelines (PHI minimization, RBAC, encryption) and scaling via Docker/Kubernetes/Azure ML, plus orchestrating ETL/ML workflows with Airflow and Kubeflow; also built an LLM-driven clinical coding assistant at Centene with measurable performance metrics.”

“Backend engineer at Cerebrone.ai building cloud-native Flask microservices for an AI-driven automation platform on GCP (Cloud Run/App Engine), including dedicated inference services integrating OpenAI and internal ML pipelines. Demonstrated strong performance and scalability wins across Postgres/SQLAlchemy optimization, multi-tenant (healthcare/HIPAA-grade) data isolation, and high-throughput background processing with Celery/Redis/RabbitMQ, with multiple quantified latency/CPU/throughput improvements.”

“Built and deployed a production RAG-based document Q&A system on Azure OpenAI to help business teams search thousands of PDFs/Word files, using Qdrant vector search, MongoDB, and a Flask API. Demonstrates strong production engineering (streaming large-file ingestion, parallel preprocessing, monitoring/retries) plus systematic prompt/embedding/chunking experimentation to improve accuracy and reduce hallucinations, and has hands-on orchestration experience with ADF/Airflow/Databricks/Synapse.”

“ML/NLP practitioner at Morf Health focused on unifying fragmented healthcare data by linking structured patient/encounter records with unstructured clinical notes. Has hands-on experience with transformer embeddings, vector databases, and domain fine-tuning, plus rigorous evaluation (precision/recall) and human-in-the-loop validation with clinical SMEs to make pipelines production-grade.”

“Backend engineer who led major modernization efforts at GoDaddy, migrating legacy Perl services to Python/FastAPI with an incremental rollout strategy, containerization (Docker/Kubernetes), and CI/CD (Jenkins/GitHub Actions). Strong focus on secure, reliable API design (JWT, RBAC, PostgreSQL row-level security), rigorous testing, and data integrity—plus experience hardening an automated web-scraping pipeline against changing site structures and downtime.”

“Backend/AI engineer who built a Spring Boot timesheet API on AWS (Postgres, Docker, Nginx) used by hundreds of daily users and resolved severe deadline-driven latency/5XX incidents via query optimization, connection pool tuning, and Redis caching. Also shipped application-layer LLM features (Mistral + LangChain chatbot) and designed a Planner/Executor/Verifier troubleshooting agent with verification-based guardrails to prevent hallucinated root-cause analyses.”

“Backend engineer with recent Barclays experience building a Python asyncio + Kafka risk reporting service for trading desks, including a major refactor from blocking batch processing to event-driven incremental pipelines to restore intraday/EOD performance. Also shipped an applied AI feature using OpenAI fine-tuning to classify risk-breach severity and generate trader/risk-manager summaries with robust retry/fallback handling, plus demonstrated strong database/query optimization (triggers, materialized views, partial indexes) in a risk-limits/breaches domain.”

“AI/ML engineer at Cigna Healthcare building a production, HIPAA-compliant LLM-powered clinical insights platform that summarizes unstructured medical notes using a fine-tuned transformer + RAG on AWS. Demonstrates strong end-to-end MLOps and cloud optimization (distillation, Spot/Lambda/Auto Scaling) with quantified outcomes (~28% accuracy lift, ~40% less manual review, ~25% lower ops cost) and strong clinician-facing explainability via SHAP and dashboards.”

“Backend-leaning full-stack engineer with production experience in both healthcare (claims enrichment/interoperability at Abacus) and finance (Goldman Sachs pricing/risk APIs + React dashboards). Built an event-driven AI grading platform using Postgres Debezium CDC + Kafka + FastAPI on AWS that cut manual grading ~70% and served 1000+ students, with strong emphasis on reliability, testing, and performance tuning.”

“Azure cloud platform engineer with strong enterprise Linux operations background who designs multi-region HA/DR on Azure (and AWS) using Azure Site Recovery, Traffic Manager, AKS autoscaling, and geo-replicated Azure SQL. Built secure Azure DevOps CI/CD pipelines for .NET/Python microservices to AKS/VMs and provisions full environments via Terraform modules with remote state, drift checks, and staged rollouts; has not directly owned IBM Power/AIX at scale.”

“Cofounded a travel app and built a production place recommendation + review system end-to-end using Next.js App Router and TypeScript, including Postgres-backed APIs and post-launch monitoring. Uses structured logging with Sentry and Vercel Analytics to diagnose issues and validate performance improvements, and has some exposure to Temporal-based workflow orchestration with retries/idempotency.”