Pre-screened and vetted.
Mid-level Software Engineer specializing in cloud platforms and AI-integrated full-stack development
“Backend engineer who built Flask-based internal APIs supporting GenAI-driven provisioning/diagnostics (Outpost/AWS Outposts-like environment), with deep hands-on optimization across Postgres/SQLAlchemy (2s to <200ms endpoint improvement). Experienced integrating ML/LLM workflows via AWS SageMaker and Bedrock, and designing multi-tenant isolation plus high-throughput Redis-backed background task pipelines (minutes to seconds).”
Mid-Level Java Developer specializing in FinTech microservices
“Backend/platform engineer with deep payments experience who built and operated a real-time transaction routing service end-to-end on AWS (Spring Boot, PostgreSQL/RDS, Redis, Kubernetes), delivering ~40% latency reduction and 99.99% uptime via strong resiliency and observability practices. Also productionized an internal LLM-powered RAG knowledge assistant with guardrails and a user-feedback-driven evaluation loop, and has led incremental monolith-to-microservices modernization using Strangler Fig and shadow traffic.”
Junior Software Engineer specializing in scalable distributed systems and cloud platforms
“Backend engineer with experience at UnitedHealth Group redesigning a high-traffic Spring Boot microservice from blocking to reactive architecture during peak season, cutting median latency by 47% for a service used by ~10M customers annually. Strong in Kubernetes-based deployment/scaling and pragmatic rollout strategies (blue-green/incremental traffic shifting) with performance and database troubleshooting.”
Mid-level AI/ML Engineer specializing in recommender systems, NLP, and cloud ML
“AI/ML engineer who has shipped both a safety-critical mental health RAG chatbot (Mistral 7B + Pinecone) with automated faithfulness/toxicity monitoring and a deep Q-learning investment recommendation engine at Lincoln Financial Group. Strong in production MLOps and orchestration (AWS Lambda/CloudWatch/SageMaker, Docker, AKS) and in translating regulated-domain requirements (clinical reliability, fiduciary duty) into measurable model constraints and monitoring.”
Director-level Cloud/Platform Engineer specializing in Kubernetes, AI systems, and distributed infrastructure
“Cloud/platform engineering lead with deep Azure AKS and SRE/observability expertise who migrated an EY enterprise SaaS platform from monolith to cloud-native microservices, supporting 10+ products and ~$200M annual revenue with ~99.9% uptime. Also building an open-source Kubernetes-native AI agent orchestration platform (AgScale) in Go with CRDs/controllers, policy/tool governance, token budgets, and production-grade monitoring.”
Junior Full-Stack Developer specializing in cloud-native microservices
Junior GenAI/ML Engineer specializing in LLM agents and production NLP systems
Mid-level Java Full-Stack Developer specializing in cloud-native microservices
Mid-Level Software Engineer specializing in cloud-native microservices and real-time ML pipelines
Senior Software Engineer specializing in cloud-native microservices and FinTech platforms
Senior Software Engineer specializing in Cloud, DevOps, and Infrastructure as Code
Senior Software Engineer specializing in Cloud DevOps & Platform Engineering
Senior Software Engineer specializing in cloud-native distributed systems and AI/ML platforms
Mid-level Full-Stack Developer specializing in AI-driven FinTech platforms
Senior DevSecOps & Observability Engineer specializing in cloud automation and STIG compliance
Mid-level DevOps Engineer specializing in cloud-native CI/CD and Kubernetes
Mid-level Full-Stack Software Engineer specializing in cloud-native and AI-driven applications
Senior Site Reliability Engineer specializing in multi-cloud, Kubernetes, and observability
Executive Engineering Leader specializing in cloud, DevSecOps, and large-scale platform modernization
“Co-founded a Digital Loss Prevention (DLP) startup and raised $6M in seed funding by showcasing a controlled, laptop-based technology demo. Post-funding, drove MVP planning and execution by sequencing operations and assembling a team to build an appliance MVP, using an iterative build/evaluate/visualize approach.”