Pre-screened and vetted.
Mid-level Full-Stack Java Developer specializing in cloud and healthcare systems
Senior AI/ML Engineer specializing in healthcare LLMs and conversational AI
Senior AI/ML Engineer specializing in healthcare and fintech AI systems
Senior Software Engineer specializing in AWS serverless backend and data engineering
Mid-level AI/ML Engineer specializing in cloud MLOps and GenAI for fraud detection
Mid-level Software Engineer specializing in distributed systems and full-stack development
Senior AI Engineer specializing in healthcare and FinTech AI systems
Mid-level Full-Stack Software Engineer specializing in cloud-native and data platforms
Staff AI Systems Engineer specializing in multi-agent and distributed platforms
Senior AI & Systems Architect specializing in ML infrastructure and FinTech
Mid-level Full-Stack Developer specializing in Java/Spring Boot, React, and cloud microservices
“Backend engineer with hands-on experience building Python/Flask microservices using PostgreSQL/SQLAlchemy, JWT auth, Docker, and GitHub Actions CI/CD. Strong in performance and scalability work—migrated heavy processing to Celery/Redis, tuned queries with EXPLAIN ANALYZE and indexing, and delivered 50%+ API latency reduction. Also integrates AI workflows (OpenAI APIs) with batching/caching/fallbacks and has implemented multi-tenant data isolation patterns.”
Senior Data Engineer specializing in cloud big data pipelines and real-time streaming
“Amazon data engineer who built a real-time fraud detection pipeline for AWS Lambda, tackling multi-region telemetry quality issues and scaling stream processing for billions of daily requests. Strong in production-grade data/ML workflows on AWS (EMR, Glue, Kinesis, SageMaker) with hands-on entity resolution and anomaly detection.”
Senior Software Engineer specializing in cloud-native backend and web/mobile apps
“Backend engineer with Tesla experience building Python-based, serverless microservices for a supply chain portal, including a MongoDB-backed tracking/logging system and a reconciler Lambda to manage retries and failures. Has hands-on Kubernetes (EKS) and GitOps (Argo CD) experience, plus real-time Kafka pipelines for fleet/IoT telemetry and proxy-based migrations from monolith systems to AWS databases.”
Mid-level Software Engineer specializing in cloud data platforms and distributed systems
“Backend/data engineer with production experience building FastAPI services with strong reliability patterns (circuit breaker, rate limiting, caching, graceful degradation) and JWT/OAuth2 auth. Has delivered AWS EKS deployments via Terraform with Secrets Manager/IRSA and HPA autoscaling, and built Glue/Spark ETL pipelines on S3 Parquet with schema-evolution and idempotent reruns; also demonstrated measurable SQL tuning impact (20–30s to <10s).”
Senior DevSecOps & Cloud Security Engineer specializing in AWS and application security
“IBM Power/AIX infrastructure engineer who has owned a large enterprise footprint (40 Power8/9 frames, 400+ AIX LPARs) with deep hands-on VIOS/HMC, NIM, performance tuning, and PowerHA recovery. Demonstrated high-impact incident response (avoided DB reboot saving ~4 hours; restored clustered services in <20 minutes) plus strong RCA and preventative remediation. Also brings modern DevOps/IaC experience building GitHub Actions pipelines and Terraform-managed AWS EKS/VPC/RDS/S3 environments.”
Mid-level Full-Stack Developer specializing in cloud microservices and AI-driven FinTech
“Stripe engineer who shipped an end-to-end merchant fraud insights dashboard, spanning Spring Boot/Kafka risk-scoring services and a React+TypeScript UI. Focused on low-latency, high-volume transaction processing and production operations on AWS (EKS/CloudWatch), including handling a real traffic-spike latency incident via query optimization, indexing, and rate limiting.”
Mid-level Backend & Reliability Engineer specializing in AWS, Kubernetes, and automation
“Meta engineer focused on reliability/operations tooling who built a unified real-time health dashboard and scalable telemetry pipelines (AWS + Datadog) for thousands of devices. Also shipped an internal LLM-powered knowledge assistant using RAG over wikis/runbooks/logs with strong guardrails and a rigorous eval loop that drove measurable accuracy improvements via automated doc ingestion and embedding updates.”