Vetted Spring Boot Professionals

“Backend developer (recent co-op at Ticker) building and architecting financial backend services with near real-time data needs, including third-party API integrations. Improved performance and reliability via Redis caching (tiered refresh + TTL) and PostgreSQL query tuning (EXPLAIN ANALYZE + composite indexes), and has exposure to AI-agent/RAG concepts for validating stock-market information against trusted sources.”

“Backend-focused TypeScript/Node.js engineer who owned a production microservice for transactional workflows in a React + microservices platform, integrating REST and Kafka event processing. Emphasizes operability and correctness (idempotency keys, exponential backoff retries, DLQs, centralized logging/metrics/alerts) plus strong API DX via versioning and Swagger/OpenAPI with improved error contracts based on developer feedback.”

“Full-stack engineer with healthcare/AI platform experience (Humana), owning an end-to-end high-risk patient prediction feature from React dashboards through FastAPI/TensorFlow real-time inference to AWS EKS operations. Emphasizes production reliability and contract-driven APIs (OpenAPI + generated TS types), plus strong data integration patterns (Kafka, idempotency, DLQs, backfills) in regulated, high-traffic environments.”

“Full-stack engineer at an early-stage startup (AirKitchenz) who owned the hourly booking/availability and first paid booking flow end-to-end—React/TypeScript frontend, Node backend, Postgres modeling, and Stripe payments/webhooks. Experienced operating production on AWS (EC2/Elastic Beanstalk, Docker, RDS, CloudWatch) and building reliable, idempotent integrations while iterating quickly in a pre-PMF environment through direct host/renter feedback.”

“Built a production LLM/RAG document extraction and game/quiz content workflow using LLaMA 2, LangChain/LangGraph, and FAISS, achieving ~94% accuracy and reducing turnaround from hours to minutes. Demonstrates strong applied MLOps/orchestration (CI/CD, MLflow, Databricks/PySpark), robust handling of noisy/variable document layouts (layout chunking + OCR fallbacks), and practical reliability practices (human-in-the-loop routing, drift monitoring, A/B testing).”

“Backend/data engineer with production experience in financial payroll, tax, and compensation platforms, building Python microservices and AWS-based data pipelines for high-volume, peak-driven workloads. Strong reliability focus (OAuth2 auth, retries/timeouts, structured logging, incident response) and proven performance wins, including cutting complex report queries from ~8 minutes to under 30 seconds.”

“AI/LLM engineer who has shipped production RAG chatbots using LangChain/OpenAI with FAISS and FastAPI, focusing on real-world constraints like context windows, concurrency, and latency (reported ~40% latency reduction and <2s average response). Experienced orchestrating AI pipelines with Celery and fault-tolerant long-running workflows with Temporal, and has applied NLP model tradeoff testing (Word2Vec vs BERT) to drive measurable accuracy gains.”

“At Unar Labs, built and operationalized LLM capabilities inside a cloud-native SaaS product, emphasizing production reliability (fallbacks, observability, cost/latency/quality monitoring) and iterative improvement from user feedback. Also acts as a customer-facing technical lead—running developer demos/workshops and supporting sales through discovery, pilots/POCs, and technical walkthroughs to drive production adoption.”

“Python backend engineer (BetterWorld Technology) who owns microservice systems end-to-end on Azure, including Kubernetes deployments, CI/CD, and production monitoring/alerting. Has hands-on experience integrating SQL/NoSQL (including Cosmos DB with vector search/graph workflow) and has built a Kafka + Spark Streaming pipeline to Snowflake with a reported 40% latency reduction.”

“Software engineer experienced delivering customer-facing, real-time industrial monitoring dashboards (motors/shafts/turbines) by partnering directly with end users to refine charts, alerts, and performance. Strong in API/platform integrations and production troubleshooting—uses feature flags, logging, validation/mapping, containerization, and performance testing to keep systems stable while iterating quickly.”

“DevOps engineer (State Farm) with hands-on ownership of Python backend services and data pipelines, deploying microservices and workers on Kubernetes using GitOps (Argo CD). Has led complex cloud-to-on-prem/hybrid migrations with staged cutovers and rollback planning, and built Kafka-based real-time streaming pipelines with schema governance, autoscaling, and strong observability.”

“Backend/cloud engineer with hands-on ownership of a climate data extraction pipeline (BeautifulSoup + Pandas ETL + CRON) that automated 50k+ monthly data points and removed ~20 hours/week of manual work. Also built a multi-AZ Kubernetes deployment for a Node.js system using Terraform and GitHub Actions (blue-green, rollbacks) and has Kafka/FastAPI experience from a healthcare plan management project.”

“Backend/platform engineer at Aryaka Networks who built a centralized resiliency and security Spring Boot library to standardize Keycloak RBAC and fault-tolerance across 25+ Kubernetes-migrated microservices. Uses profiling and observability (Prometheus/Grafana) to drive measurable performance and reliability gains (25% faster APIs, 70% faster environment setup) and accelerates adoption via golden-path starter repos and Swagger/OpenAPI live docs.”

“Full-stack engineer with payments/settlement domain experience who modernized a payment tracking workflow from REST to GraphQL and delivered a production payment status dashboard using Next.js App Router + TypeScript. Strong in performance and reliability work (Postgres indexing/Explain Analyze, Redis caching, Datadog observability) and in durable event-driven processing with Kafka (DLQs, idempotency, reconciliation, event replay).”

“GenAI/LLM engineer who built and productionized a 0-1 application (EMULaiTOR at Lumanity) combining qualitative + quantitative data using Postgres/pgvector RAG and prompt engineering, deployed with Azure backend and AWS-hosted frontend. Demonstrates strong production instincts (latency reduction via region alignment, autoscaling/health checks) and hands-on agent/tool-call debugging, plus experience enabling sales and winning a large pharma client.”

“Backend/security-focused engineer from VeroTX who built an IdP service (Spring Boot + MongoDB on GCP) for an AI workflow platform and drove major latency improvements via caching and query/index optimization. Also shipped an AI loan-processing agent using LangChain/LangGraph, owning the document ingestion + vector database layer and designing a reliable multi-step workflow with retries, monitoring, and human-in-the-loop safeguards.”

“Full-stack engineer with healthcare and insurance domain experience who has owned production systems end-to-end (React/Next.js, FastAPI/Node, Postgres, AWS SNS/SQS, Docker, CI/CD) and delivered measurable impact (30% faster data processing). Also productionized an LLM-powered clinical data assistant using RAG + a vector database with guardrails and evaluation loops, cutting analyst lookup time by ~30–40%, and has experience modernizing monoliths to microservices with feature-flagged, low-regression rollouts.”

“Backend/full-stack engineer who has owned production TypeScript systems in both fintech-style transaction/rewards flows and HIPAA-regulated healthcare platforms. Deep focus on correctness and reliability (idempotency, retries/DLQs, reconciliation, observability) plus strong infra automation (Docker/Terraform/CI-CD) and measurable performance wins (40% query improvement, 90% test coverage).”

“Full-stack engineer working at the intersection of product and infrastructure, building developer-facing interfaces for AI voice agents in XR/immersive environments plus telemetry-heavy analytics dashboards. Experienced in Postgres telemetry data modeling and performance tuning, and in designing durable multi-step LLM pipelines with idempotency, retries, and strong observability; has operated in fast-moving startup-like teams (Biocom, HandshakeAI).”

“Built and shipped both a solo real-time multiplayer Spades game (TypeScript monorepo with shared client/server engine) and a production internal LLM-powered document Q&A tool for a SaaS company. Demonstrates strong RAG pipeline design (Pinecone + embeddings + reranking), rigorous eval/regression practices, and pragmatic data ingestion/observability work across Confluence, Notion, and messy PDFs/OCR—backed by clear metric improvements (P@1 61%→78%, escalations 40%→22%).”

“Backend engineer with experience modernizing high-traffic subscription and payment systems (TCS) by moving to event-driven Spring Boot microservices with Kafka, adding idempotency/state management to eliminate duplicate processing. Built and scaled FastAPI services for AI automation workflows (360DMMC) with versioned contracts, JWT security, and strong observability, and has led live refactors using feature flags, parallel runs, and data reconciliation.”

“Backend/data engineer focused on AWS-based, low-latency event processing for market data and social-signal sentiment systems. Has led a monolith-to-event-driven migration with feature-flagged incremental rollout, and emphasizes production-grade security (OAuth2/JWT, secrets management, Supabase RLS) and data integrity (deduplication/idempotency) under high-volume spike conditions.”

“Backend engineer who built a high-stakes, privacy-first platform at be Still Analytics for survivors of domestic violence, emphasizing anonymity, security, and reliability. Experienced with GenAI backends (LangChain + AWS Bedrock) including RAG to prevent hallucinations, plus cloud-native scaling (Docker/Kubernetes) and cost-saving migrations from legacy VMs to serverless (30% reduction).”

“Backend/full-stack engineer (Cognizant) who owned a customer-facing document validation platform for onboarding/compliance and emphasized fast, reliable delivery through CI/CD automation (Jenkins/AWS) and containerized releases (Docker/Kubernetes). Also built an internal support dashboard that replaced manual log checks with real-time error visibility, significantly reducing investigation time, and has hands-on experience scaling microservices with idempotency, retries/DLQs, and distributed logging.”