Vetted Amazon ECS Professionals

“Backend/data platform engineer with production experience at Walmart and Molina Healthcare, building Python microservices on AWS (EKS + Lambda) for real-time inventory and recommendation systems. Strong in reliability/observability and incident leadership, plus modernizing legacy healthcare workflows and building resilient AWS Glue/PySpark pipelines with schema evolution and data quality controls.”

“Infrastructure/DevOps-focused engineer working across Linux-based enterprise platforms that include IBM Power/AIX in a broader OpenShift/Kubernetes and cloud ecosystem. Built Azure DevOps CI/CD for containerized deployments and resolved a production deployment failure by tracing ImagePullBackOff to outdated registry credentials in Kubernetes secrets. Uses Terraform (with modular structure) plus Ansible to provision and standardize production environments with pipeline-based validation.”

“Insurance-domain engineer with hands-on mainframe (CICS) + modern cloud (AWS) experience who built a life-insurance quoting service end-to-end, including JSON-to-mainframe middleware and REST APIs with detailed business error handling. Led a tax-form legacy modernization by moving disorganized mainframe extracts into an event-driven S3/Lambda/Python cleanup pipeline to reduce mainframe load and isolate failures.”

“Backend-focused engineer building customer support/order-tracking platforms with Java 17/Spring Boot microservices and a React/TypeScript frontend. Deep experience running event-driven systems on Kubernetes (Kafka, Redis, MySQL) with strong observability (Prometheus/Grafana/Splunk), SLOs, and safe deployment practices (feature flags, canaries). Also built an internal monitoring/debugging dashboard that consolidated metrics and logs for on-call engineers and was adopted by other teams to speed incident response.”

“Backend/platform engineer who owned high-volume Java/Spring Boot microservices on AWS (Kafka + RDS/DynamoDB) and has hands-on experience debugging complex production latency incidents across DB, JVM/GC, and async consumers. Also shipped applied AI features for ops, including an LLM-powered log analysis assistant and an incident-response agent with strong safety guardrails (schema-validated tool use, retries/backoff, and human-in-the-loop escalation).”

“Backend/AI engineer at Intuit who built a production AI-powered case assistant for support agents (FastAPI on AWS EKS) combining Postgres case data, OpenSearch retrieval with embedding reranking, and internal LLMs. Improved peak-season reliability by diagnosing P95/P99 timeout spikes and cutting P95 latency from ~800ms to <400ms via composite indexing, keyset pagination, connection pool tuning, and caching, while adding grounded-generation guardrails (evidence packs, confidence thresholds, fallbacks, human-in-the-loop).”

“Bank of America engineer/product owner who built a real-time transaction insights and spending categorization platform using React/TypeScript and Spring Boot microservices with Kafka. Deep experience in event-driven architectures, performance tuning at peak banking loads, and reliability patterns (SLOs, observability, feature flags, DLQs). Also created an internal monitoring/alerting tool adopted across engineering and ops, cutting incident response time by 40%+.”

“Built a production LLM-driven personalization microservice for realtor.com using LangChain/LangSmith with an MCP tool layer and RAG to generate schema-constrained ranked listings in real time, replacing a rule-based engine and improving engagement/lead conversion. Also owned an ambiguous cross-channel identity initiative, implementing an identity graph via Twilio Unify with required SDK and data-warehouse integration.”