Vetted Infrastructure as Code Professionals

“AWS Cloud DevOps Engineer focused on production Linux environments, building secure CI/CD pipelines (Jenkins/GitHub) to deploy Dockerized services to AWS ECS and automating infrastructure with Terraform/CloudFormation. Strong in operational troubleshooting and scaling (CloudWatch-driven performance remediation, Auto Scaling/ELB, multi-AZ HA patterns), but explicitly does not have IBM Power/AIX or PowerHA/HACMP experience.”

“AWS-focused Python backend/data engineer who builds production analytics APIs and ETL pipelines using API Gateway, Lambda, Step Functions, ECS, Glue, S3, and RDS. Strong in operational reliability and performance tuning (including SQL indexing/partitioning) and has modernized legacy SAS statistical processing into validated Python services with phased rollouts and stakeholder sign-off.”

“Backend/data engineer with strong AWS production experience spanning serverless APIs and containerized workers (Lambda, API Gateway, ECS) plus data pipelines (Glue, S3, Athena/Redshift). Has modernized legacy SAS/cron batch systems into Python/AWS with parallel-run parity validation and low-risk cutovers, and has owned ETL incidents end-to-end (CloudWatch detection, backfills, and preventative controls). Targeting $130k–$150k base and strongly prefers remote, with occasional Bethesda onsite acceptable.”

“Software engineer/product-minded builder who owns customer-facing products end-to-end and ships in 1–2 week increments using CI/CD, automated testing, and feature flags. Built a TypeScript/React/Node platform that cut page load times by 40% and scaled to 3x concurrent users, and designed RabbitMQ-based microservices with Prometheus/Grafana monitoring. Also delivered an internal real-time support analytics dashboard that reduced response times by 30%.”

“Backend/LLM engineer who ships production internal tooling end-to-end: automated data-request processing with monitoring-driven improvements (better error diagnostics and lower latency via query/index tuning). Also built a RAG-based internal Q&A system over company docs and operational logs with guardrails (similarity thresholds, fallbacks, response limits) and an eval loop using real user queries and human review to drive prompt/retrieval changes.”

“Backend/data platform engineer who owned a near-real-time patient feedback ingestion system, building a FastAPI + Kafka service with Snowflake/Airflow orchestration. Demonstrates strong production Kubernetes/GitOps practices on AWS EKS (Helm, Argo CD, Sealed Secrets) and solved real-time data integrity issues via idempotent processing with Redis.”