Pre-screened and vetted.
Senior Engineering Leader specializing in FinTech infrastructure and cryptographic security
Senior AI Engineer specializing in LLM systems and scalable backend platforms
Mid-level Software Engineer specializing in Python, distributed systems, and AI backend services
Senior Software Engineer specializing in backend, distributed systems, and computer vision
Senior Machine Learning Engineer specializing in GenAI, NLP, and recommendation systems
Senior Software Engineer specializing in AI backend platforms and FinTech systems
Intern Software Engineer specializing in distributed systems and cloud infrastructure
“Built and operated a production warehouse metadata collection platform at Sigma Computing, integrating Go/gRPC services with a TypeScript backend and MySQL, with strong emphasis on idempotency, retries, bounded-concurrency job queues, and Datadog-based observability. Also created Kurral (kurral.com), an AI agent runtime security and observability/governance SDK/proxy concept, iterating via pilot-customer feedback and market research; targeting founding engineer roles with $180–200k base and ~2–5% equity.”
Senior Full-Stack Engineer specializing in backend systems and AI applications
“Candidate is deeply focused on AI-native software development, using a deliberate planner/implementer agent workflow with tools like Cursor, Claude, and Kimi. They also built a personal project called Config Proctor, an AI-agent-driven Terraform/AWS self-healing system that identifies infrastructure configuration gaps and proposes fixes.”
Entry-level Software Engineer specializing in full-stack and AI systems
“Frontend-leaning full-stack engineer who described owning an artist search and detail experience across UI, backend integrations, and data modeling. They show practical strength in scalable React architecture, TypeScript safety, and performance tuning, with a product-minded approach to shipping 0→1 features quickly and iterating after launch.”
Senior Software Engineer specializing in FinTech and distributed systems
“Backend/AI engineer who has built a rule-service platform on AWS and evolved it into an agentic RAG system using LangChain, ReAct, tool calling, and LLM-as-judge review. Notable for combining heavy AI-assisted development with production safeguards like manual CR, CloudWatch monitoring, fallback strategies, benchmark testing, and user-feedback-driven model improvement.”
Senior Backend Engineer specializing in distributed microservices and event-driven systems
“Backend engineer with production experience building a high-scale notification pipeline (~20M/day) using Java/Dropwizard with Kafka and Azure Queue, including DLQ/poison-message handling and the outbox pattern for reliability. Also led a batch-based migration of Yammer Messaging user data from PostgreSQL to Azure Cosmos DB for global multi-region scale, addressing throttling and network failures via retries, escalation policies, and dynamic throughput tuning.”
Senior Software Engineer specializing in backend APIs, search, and distributed systems
Executive engineering leader specializing in capital markets, risk systems, and FinTech
Senior AI/ML Engineer specializing in personalization, recommendations, and forecasting
Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines
Intern Software Engineer specializing in full-stack and distributed systems
Mid-level Full-Stack Python Engineer specializing in AI search and distributed systems
Senior Software Engineer specializing in backend platforms and data systems
Mid-level Data Engineer specializing in big data platforms and analytics infrastructure
Senior Software Engineer specializing in backend distributed systems and FinTech
Engineering Manager and ML/Data Architect specializing in scalable data platforms and personalization
“Hands-on engineering manager at a marketing company leading a highly senior, distributed team (10 direct reports) while personally coding ~60–70% and owning end-to-end architecture across three interconnected products. Built agentic CRM automation and a reinforcement-learning-driven distribution layer for channel spend/bidding, with a strong focus on scalable design and observability (Prometheus/APM/logging) enabling frequent releases and few production incidents.”