Vetted Terraform Professionals

“AI/ML engineer who has shipped production AI systems end-to-end, including an automated multi-channel (Gmail/WhatsApp/voice) candidate interviewing workflow and an enterprise RAG knowledge search platform. Demonstrates strong production rigor (monitoring, A/B tests, guardrails, schema validation, shadow testing) with quantified impact: ~60–70% reduction in interview evaluation time and ~20–30% relevance gains in RAG retrieval.”

“Full-stack engineer with startup ownership experience (Aiir) building 15+ TypeScript/Go microservice APIs on GCP Cloud Run with Kafka-based async event streaming and React CRM integrations for billing/analytics. Strong post-launch operator who tuned Oracle performance (partitioning/indexing/query optimization) and validated a 23% retrieval-time reduction via AWR, and has a quality/DevSecOps mindset (94% Pytest coverage, GitHub Actions, SonarQube, Twistlock, CloudWatch) including migrating 18+ production CI/CD pipelines.”

“Infrastructure-focused engineer with Vonage experience modernizing early-stage cloud architecture (Terraform modularization, blue-green deployments, containerization, and zero-downtime database migration planning to Aurora). Also built a local end-to-end side project, Vastu AI, combining a custom-trained YOLO model (Roboflow-labeled data) with a locally hosted LLM via Ollama to generate a vastu compliance report from floor-plan images.”

“Full-stack engineer with fintech and AI feature experience who shipped an AI-powered project summary module in Next.js (App Router + TypeScript) with secure server-side fetching and route handlers to a FastAPI backend, then owned monitoring and performance fixes in production. Demonstrated measurable UX wins (30% faster dashboard loads) and strong backend fundamentals (Postgres indexing/EXPLAIN ANALYZE, SQS-orchestrated idempotent reconciliation workflows with DLQs and retries).”

“Unreal Engine UI engineer focused on scalable, production-ready UI architecture (C++/Slate/UMG/CommonUI) with strong designer enablement via decoupled, interface-driven patterns and MVVM. Demonstrated measurable performance wins: replaced 200+ per-frame Blueprint bindings to cut UI prepass/paint from 4.2ms to 0.5ms and reduced VRAM by ~120MB using texture streaming proxies.”

“Built and productionized a Python/LLM-based pipeline at Catalyst Solutions to automate healthcare RFP processing, turning unstructured documents into validated JSON/Excel with schema validation, confidence scoring, and human-review routing. Delivered major operational impact (hours-to-minutes processing, ~60% efficiency gain; 50+ RFPs processed) and modernized legacy scripts into a staged, more reliable architecture using incremental refactoring and fallback comparisons.”

“Backend/data engineer with production experience building real-time IoT telemetry pipelines for wind/solar assets at Siemens (FastAPI on Azure Event Hubs/Service Bus, Cosmos DB + SQL Server) and deploying GPS/fleet telematics microservices on AWS ECS Fargate with Terraform and blue/green CI/CD. Demonstrated strong reliability and performance chops, including a 30s-to-<100ms SQL optimization and owning a Kafka pipeline incident resolved in ~20 minutes.”

“LLM/RAG practitioner who productionized an LLM-driven customer communication and transaction understanding system at PayPal, emphasizing privacy/compliance guardrails and large-scale data normalization. Experienced in real-time debugging of hallucinations via retrieval pipeline tuning and in leading hands-on developer workshops and sales-aligned POCs to drive adoption.”

“Software engineer with strong end-to-end ownership of search and listing systems (React/TypeScript frontend with Node.js + Spring Boot backends), focused on shipping fast while managing risk via feature flags, testing, and metrics. Demonstrated measurable UX/performance wins (reduced latency and search abandonment) and built internal observability tooling (dashboard + alerts) that improved incident response. Experienced with microservices reliability patterns including idempotency and dead-letter queues.”

“Full Stack Engineer in fintech (JPMorgan) who owns products end-to-end across React UIs and Spring Boot/Kafka backends, with a strong track record of shipping quickly while maintaining reliability via testing, monitoring, and feature flags. Has hands-on experience scaling microservices for high-volume transactions and debugging production latency using ELK/CloudWatch, plus built an internal Python/Flask automation tool adopted by backend engineers to speed API validation and debugging.”

“Software engineer at Cimpress owning end-to-end transactional pages for Pens.com (e-commerce). Built and integrated new discount experiences in a React/TypeScript + Node.js stack, focusing on modular component architecture to reduce tight coupling and avoid breaking existing functionality; prioritizes roadmap work using performance and conversion metrics.”

“Interned at Rivian where they built and deployed a production Whisper-based ASR + LLM real-time event labeling pipeline to help autonomous-vehicle engineers diagnose failures and route issues to triage teams. Also built a stateful multi-agent "Code Partner" developer assistant using LangGraph/LangChain (planner/router/coder/critique/tester) with evaluation, adversarial testing, and stakeholder-friendly communication practices.”

“Hands-on architect/technology leader who builds prototypes (including Agentic AI wellness/biomarkers) and then scales teams to execute. Led a ~$400M global e-commerce transformation spanning 95 countries with active-active US/EU multi-region resilience, microservices/MFE (MACH), and strong security patterns (service mesh + API gateway + Ping Identity), plus modern data foundations (customer hub/MDM/Snowflake, data fabric/medallion).”

“Software engineer with Capital One experience contributing to shared internal “open-source style” JavaScript/React/TypeScript libraries (component library and hooks/utilities). Drove measurable performance gains (~25% improvement) by refactoring hooks to prevent unnecessary re-renders, and improved adoption via stronger documentation, testing (Jest), semver discipline, and code review/PR workflows; also stabilized a backend service by adding monitoring and automated tests in an unstructured project.”

“Early-career engineer who owned an end-to-end objective assessment/coding contest platform at an edtech startup, using Postgres + S3 and Redis (queues + ZSET) to decouple and scale code submission processing with worker sandboxes. Also implemented idempotency controls and set up monitoring and CI/CD while the rest of the team focused on curriculum.”

“Built a production internal RAG-based Q&A assistant at Huawei for ~4,000 engineers over a 12M-document Elasticsearch corpus, replacing link-only search with synthesized answers and achieving 87% user acceptance while keeping hallucinations under 0.4%. Pairs rigorous offline benchmarking (RAGAS, PR-gated F1 improvements) with human A/B testing and OpenTelemetry-based production monitoring, and also has strong Kubernetes/SRE experience orchestrating 50+ gRPC services with major MTTR and pager-fatigue reductions.”

“DevOps/data-automation professional with HPE experience who has deployed containerized microservices to AWS EKS and built an end-to-end observability stack (Prometheus/Grafana/CloudWatch via Terraform), reporting zero-downtime deployments and ~40% faster incident response. Also extends Python ETL automation for procurement/operations teams (rules engine, validation, performance tuning) and bridges SAP ERP data into Power BI/Qlik dashboards through close on-site user collaboration.”

“Backend engineer focused on building production-ready Python services (Flask/FastAPI) with strong performance and scalability instincts—Celery/Redis background processing, robust multi-tenant isolation (Postgres RLS), and pragmatic CI/Docker operations. Demonstrated measurable DB optimization impact (cut a critical analytics query from ~1–2s to ~100–150ms) and has hands-on experience integrating LLM/ML workflows (OpenAI, LangChain, embeddings, Redis/FAISS vector stores) without degrading API responsiveness.”

“ML/NLP engineer with experience at Flatiron Health building a production NLP platform that processed millions of clinical notes, using BERT/BiLSTM-CRF and spaCy to extract and normalize entities from noisy EMR text with oncologist-in-the-loop validation. Also built scalable retail ML workflows (Spark + Kubernetes + feature store caching) and applied vector databases plus contrastive-learning fine-tuning to improve retrieval relevance and recommendations.”

“Backend/DevOps-focused engineer with healthcare and financial systems experience, including an ICU readmission risk platform delivering real-time ML scores via a secure FastAPI service (PyTorch model serving, PostgreSQL, Celery/Redis) deployed on AWS with strong observability. Has hands-on Kubernetes GitOps delivery (Helm, ArgoCD, HPA) and has supported a JPMC on-prem-to-AWS microservices migration using phased validation and blue-green cutovers, plus Kafka/Avro streaming for real-time transaction processing.”

“Senior engineering leader from Goodyear’s AndGo startup-like division who scaled the org from 12 to 30+ across pod-based teams and introduced an Architect Guild/ARD governance model. Led a 4-month Europe launch requiring AWS regional infrastructure, GDPR compliance, i18n/l10n, and new EMEA reporting pipelines, and has hands-on depth in API performance, incident response, and GraphQL/Hasura adoption to boost product velocity.”

“Open-source maintainer of KafkaJSUI, a Vue.js-based Kafka browser UI, focused on making large-topic exploration fast and responsive. Delivered major performance wins (incremental fetching, virtualized lists, WebSocket streaming, backpressure, Web Worker offloading) cutting load times to sub-200ms, and also strengthened CI and developer documentation while handling community-reported issues end-to-end.”

“Backend/automation engineer who led the design of an OOP Python test automation framework for AWS infrastructure (Behave + Jenkins), cutting regression effort from weeks to a 3–4 hour run. Has hands-on cloud and DevOps experience across AWS (boto3, ECS, AMI automation via GitHub Actions) plus data/migration work including on-prem-to-cloud Oracle Retail DB migration with rollback planning and a Kafka + ML fraud-detection streaming pipeline.”

“Hands-on technical professional experienced in taking LLM/AI-adjacent integrations from prototype to production, using customer observation to refine UX and uncover edge cases. Diagnoses workflow issues in real time using logs and Sankey-style workflow analysis, and communicates fixes with clear short/long-term plans plus proactive alerting. Also partners cross-functionally to drive adoption and cost savings, including a POC around IBM Sterling Integrator that reduced licensing costs by $30K/year.”