Pre-screened and vetted.
Senior Product Manager / Project Manager specializing in data platforms, BI, and cloud transformation
Senior QA Analyst specializing in API, integration, and data-driven testing
Senior Full-Stack Software Developer specializing in enterprise web and mobile apps
Senior Data Engineer specializing in AWS-based data pipelines and multi-tenant SaaS
Senior QA Engineer specializing in automation, accessibility, and cross-platform testing
Executive Engineering Leader (CTO/SVP) specializing in high-load platforms and GenAI/LLM systems
Executive Engineering Leader specializing in Product, Mobile, and SaaS platforms
Mid-level Full-Stack Software Engineer specializing in React/Node and cloud-native web apps
“Full-stack engineer who built and iterated a CRM dashboard at ReplyQuick by sitting with end users, prioritizing blockers, and shipping frequent updates—improving usability and performance enough to replace a spreadsheet workflow within ~2 months. Demonstrates strong security fundamentals (OAuth2/JWT + RBAC) and practical microservices experience (decoupling a CRM API from a PDF-processing service via async processing and status tracking).”
Senior Python Developer specializing in AWS, microservices, and data pipelines
“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.”
Mid-Level Full-Stack Software Developer specializing in Java/Spring microservices and cloud
“Backend engineer who owned and shipped a campaign analytics API (FastAPI/Postgres/Redis/Celery) with ingestion from Instagram/YouTube, JWT auth, tests, and Docker deployment; improved performance from >1s to <150ms using precomputed aggregates and composite indexes. Experienced with Kubernetes GitOps using GitHub Actions + ArgoCD (zero-downtime rollouts, one-click rollbacks), Prometheus/Grafana observability, hybrid cloud-to-on-prem migrations, and real-time notification streaming via Redis Pub/Sub + WebSockets.”
Mid-Level Software Engineer specializing in Java microservices and cloud-native AWS development
Mid-level QA & Data Processing Engineer specializing in sports motion capture and gaming platforms
Mid-level AI/ML Engineer specializing in cloud-native data pipelines and RAG systems
Mid-level Software Engineer specializing in backend systems, APIs, and cloud microservices
Mid-level Full-Stack Engineer specializing in cloud-native, event-driven data platforms
“Backend/data engineer with hands-on production experience building Python (FastAPI/Flask) data enrichment services secured with Okta OAuth2 and monitored via Splunk/Dynatrace. Has delivered AWS event-driven and data-migration solutions (Lambda + Kafka to EKS; Glue from on-prem Oracle to S3/data lake) and modernized Informatica match/merge logic to cloud services using parallel-run parity validation and stakeholder sign-off.”
Junior Full-Stack Software Engineer specializing in Java/Spring Boot and React
“Backend engineer (IpserLab) who owned Python services for a production quiz/analytics platform, focusing on reliability and low-latency behavior under peak load. Hands-on with Kubernetes + Docker deployments and GitHub Actions CI/CD in a GitOps-style workflow, including solving configuration drift and enabling fast rollbacks. Also implemented Kafka-based event streaming with idempotent consumers and strong observability (lag tracking, structured logging, alerting).”
Mid-level Full-Stack Engineer specializing in React, Spring Boot, and cloud microservices
“Software engineer with hands-on experience building data-intensive and 3D-processing web applications (React/Next.js/TypeScript + Node.js). Has worked in microservices using RabbitMQ for event-driven workflows and built an internal ops/engineering dashboard to monitor pipeline jobs, surface logs, and manage retries—improving visibility and reducing on-call/debug time.”