Vetted API Integration Professionals

“AI Engineering Intern at The Etherloop building the backend for a healthcare lifestyle recommendation app, including a multi-agent RAG-based system that uses curated SME data plus web search to generate personalized supplement recommendations from user lifestyle details and blood biomarkers. Evaluates against 500+ SME ground-truth profiles with ranking metrics and focuses on HIPAA-aligned deployment, privacy/security, and guardrails to reduce hallucinations and unsafe outputs.”

“AI engineer with experience taking a GPT-4-powered GenAI career coach toward production on Azure AI Foundry, re-architecting the backend with hybrid (vector + keyword) search and RAG optimizations to cut latency by 50%. Also has client-facing TCS experience building healthcare ETL pipelines and delivering error-free monthly reports, plus current work analyzing agentic system reasoning traces and guardrail drift as an AI research fellow.”

“Data engineer/software engineer who led an end-to-end ETL/ELT pipeline at Pearson processing millions of rows of student data nightly, including client-side data prep/validation, SFTP/API ingestion, staging-based SQL validation/transforms, and production loading. Built reliability features like configurable per-client validation thresholds, detailed reporting, concurrency throttling via a custom queue, and multi-source merge/backfill logic to keep nightly loads running even when sources fail.”

“Infrastructure/automation engineer with experience bridging post-acquisition environments (Pandora + SiriusXM) by building an API-driven integration to provision Debian workloads on RHV while preserving iPXE-based imaging workflows. Strong in deep debugging across virtualization/network/OS layers (e.g., resolving virtio/vCPU contention causing network/NFS issues) and in extending automation tooling via custom Ansible/Python modules. Also has exposure to biomanufacturing on-prem devices (Hamiltons, shakers) alongside AWS microservices.”

“Software engineer with JPMorgan Chase experience building a real-time operations console backend on Spring Boot/Kafka/Kubernetes and resolving peak-load latency through profiling, indexing, caching, and async processing. Also built and owned an AI-driven digital-archives metadata pipeline during a master’s at UNT using OCR + LLaMA-based prompting with validation, near-human accuracy, and human-in-the-loop guardrails.”

“Backend/platform engineer who owned an end-to-end financial data ingestion and validation system (Python/Django/FastAPI, Postgres, AWS), including large-file performance tuning, auditability, and CI/CD. Strong Kubernetes/EKS + ArgoCD GitOps practitioner and has delivered both Kafka-based real-time transaction streaming and a legacy on-prem stack migration to AWS (ECS Fargate, RDS, S3, Secrets Manager) with controlled cutovers and data consistency validation.”

“Frontend engineer experienced delivering React + TypeScript dashboards end-to-end, including real-time updates via WebSockets and scalable component architecture. Has improved inherited codebases by building shared component libraries and performance optimizations (lazy loading/memoization), reporting ~20% faster feature delivery, and ships major features with QA rigor and feature-flagged rollouts driven by analytics and user feedback.”

“Backend engineer with insurance/claims domain experience who modernized legacy claims processing systems to support AI-assisted claim review. Emphasizes production-ready API design in Python/FastAPI (schemas, async, caching, graceful degradation), strong observability with Prometheus, and layered security including JWT auth plus database row-level security (Supabase/Postgres).”

“Backend Python engineer who built core services for a telecom automation engine monitoring thousands of routers in real time and auto-generating support tickets. As the sole Intelygenz engineer on the project, they diagnosed a costly Terraform/GitLab CI/CD resource-leak issue in AWS and implemented a cleanup redesign that eliminated orphaned resources and reduced client cloud spend. Also shipped applied-AI ticket triage suggestions via API integration and built an end-to-end Gmail-to-ticket ingestion workflow.”

“Built a production AI-powered customer support Q&A system using an internal knowledge base to reduce repetitive ticket work and improve customer satisfaction, with an emphasis on source-backed answers and expert oversight. Also has experience defining deployment services in a microservices architecture and integrating large-scale APIs (including work connected to US HHS/COVID-19).”

“Cloud/ML engineer with production deployment experience on Azure (Dockerized models, managed APIs, data pipelines) who has repeatedly stabilized unreliable systems—e.g., taking an API-driven analytics pipeline from ~60% to 98% reliability and an Azure ML service from ~80% to 97% by addressing rate limits, container memory, and gateway timeouts. Also built an explainable contract-risk model for entertainment bookings (Transformers + SHAP) and integrated it into a legacy booking system via a Flask REST API, plus prior IoT work at Nissan processing CAN bus sensor streams for diagnostics/anomaly insights.”

“Full-stack developer with 4 years of experience and an MS in Computer Science who led frontend delivery for a large airline platform (booking, check-in, and payment flows) using Angular/TypeScript with a Java backend. Emphasizes quality at scale via SonarQube monitoring, E2E/regression testing, and iterative Agile collaboration with clients using Figma.”

“Customer-facing software engineer who rapidly turns business requirements into Figma prototypes and PoC applications, using workflow prioritization and frequent client reviews to stay aligned. Has hands-on experience integrating with existing authentication/user APIs, building MongoDB-backed caching, and implementing robust fallback/retry mechanisms. Comfortable working on-site with customers and resolving production issues in AWS (e.g., DNS/EC2 traffic routing) in collaboration with DevOps.”

“Solutions Architect with 5+ years leading pre- and post-sales engagements, focused on taking complex tooling from test/prototype to secure production through a structured discovery-to-deployment approach. Experienced in LLM workflow troubleshooting using tools like Langfuse/Gopher and in developer enablement via concise, hands-on workshops (e.g., Jenkins on Kubernetes at scale). Has navigated internal and external blockers to drive adoption and keep enterprise deals moving (including a Jenkins sale to Love's).”

“Full-stack engineer (HCL Tech) with 4 years building enterprise, high-throughput microservices on AWS/Azure using Java/Spring Boot and React. Demonstrated measurable performance gains (40% throughput) through Redis caching, deep SQL/query tuning, and Kafka-based async refactors, plus strong DevOps/observability practices with Jenkins/CloudFormation and Datadog/Splunk.”

“Full-stack engineer in the healthcare domain (Humana) who owned an end-to-end member portal for benefits/claims/appointments, built with React and Spring Boot microservices on AWS. Notably migrated legacy batch data flows to a Kafka streaming pipeline and tuned consumers/partitions/backpressure to improve real-time consistency and achieve ~12% processing performance gains.”

“Front-end-focused developer at a startup who also handles back-end integration, delivering customer-facing analytics dashboards from Figma designs while collaborating tightly with UX/product and running customer review cycles. Recently helped manage a risky production user-migration issue by stopping deployment, restoring deleted records from backups, and rebuilding the migration process with a safer test environment and validation.”