Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in financial risk, NLP, and MLOps
Director-level Mobile & Full-Stack Software Engineer specializing in Android and cloud-native apps
Mid-level QA Automation Engineer specializing in healthcare test automation and DevOps
Mid-level Software Engineer specializing in backend systems and AI voice platforms
Senior Data Engineer specializing in AWS-based data pipelines and multi-tenant SaaS
Mid-Level Full-Stack Java Developer specializing in Spring Boot, React, and AWS
Executive Engineering Leader (CTO/SVP) specializing in high-load platforms and GenAI/LLM systems
Junior Software Engineer specializing in backend systems, QA automation, and AI/ML
Executive Engineering Leader specializing in Product, Mobile, and SaaS platforms
Senior Full-Stack Java Developer specializing in AWS cloud and microservices
Junior Full-Stack Software Engineer specializing in web apps and AI-powered RAG systems
Staff Engineer specializing in applied AI and healthcare 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).”
Mid-level AI/ML Engineer specializing in NLP, computer vision, and recommender systems
“Built and deployed a production NLP sentiment analysis system at Piper Sandler to turn noisy, finance-specific customer feedback into scalable insights. Demonstrates strong end-to-end MLOps: fine-tuning BERT, improving label quality, monitoring for language drift, and automating retraining/deployment with Airflow and Docker (plus Kubeflow exposure).”
Mid-Level Full-Stack Software Engineer specializing in Cloud, DevOps, and Platform Engineering
“Backend/Node.js-focused engineer who improved a widely used shared config/logging utility library by fixing a real-world async race condition (single disk read under concurrency) and adding stronger validation/testing, resulting in more deterministic services and faster startup/build/CI times. Also builds internal platform automation spanning Python/Go/TypeScript with strong documentation practices and security-conscious customer onboarding (e.g., sensitive Kubernetes clusters, HashiCorp Vault access issues).”
Mid-level Data Scientist specializing in industrial IoT, predictive analytics, and generative AI
“ML/NLP engineer with Industrial IoT experience who built an end-to-end anomaly detection and GenAI explanation system: AWS (S3, PySpark, EC2/Lambda) pipelines feeding dashboards, plus transformer-embedding vector search to connect anomalies to noisy maintenance notes and past events. Demonstrated measurable impact (15% lift in defect detection; ~35% reduction in manual review; 35% fewer preprocessing errors) and strong productionization practices (orchestration, monitoring, rollback, data-quality controls).”
Mid-level Cloud/DevOps Engineer specializing in AWS automation and CI/CD
“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.”
Mid-Level Software Engineer specializing in backend microservices and cloud-native systems
“Full-stack TypeScript engineer who has owned a real-time workflow/communication platform end-to-end in production (Node/TS + React, Postgres/Redis, Kafka, Docker/CI/CD). Demonstrates strong distributed-systems pragmatism—designing for failure with retries, DLQs, idempotency keys, and atomic writes—plus operational practices like structured logging, monitoring, and zero-downtime deployments.”