Pre-screened and vetted.
Mid-level Software Developer specializing in full-stack and backend systems
Intern AI/Backend Engineer specializing in LLM agents and cloud microservices
Mid-Level Backend Software Engineer specializing in distributed systems and observability
Junior Full-Stack/Cloud Engineer specializing in AI and data-driven applications
Senior Software Engineer specializing in Healthcare IT platforms
Mid-level Backend/Data Engineer specializing in legal data pipelines and APIs
Senior AI/ML Engineer specializing in LLMs and enterprise conversational AI
Entry-level Software Engineer specializing in systems and healthcare data
Senior Software Engineer specializing in backend data platforms for FinTech
Senior Full-Stack Engineer specializing in backend, cloud, and AI systems
Mid-level Full-Stack Engineer specializing in AI platforms and FinTech
“Built full-stack and AI-driven products spanning banking KYC modernization and enterprise software testing automation. Particularly strong in productionizing LLM workflows in regulated environments, using deterministic orchestration, RAG, and human-in-the-loop controls to improve test coverage to 80% and reduce QA reporting burden by over 50%.”
Mid-Level Full-Stack Software Engineer specializing in cloud-native APIs and compliance
“Full-stack/backend engineer with healthcare and enterprise experience: built and secured AWS-hosted services for a clinical EHR product that redacts/transforms hospital patient records for pharma customers (e.g., AstraZeneca, Johnson & Johnson). At Cisco, led an incremental Ruby-to-Python/Django migration for a compliance backend, and has deep multi-tenant security experience using Postgres RLS tied to JWT plus DLQ patterns to harden data pipelines.”
Executive AI Architect specializing in enterprise cloud and FinTech solutions
“Candidate brings an operator-to-founder profile with leadership experience in IT and Business Systems and a strong grasp of how ideas become venture-backable products. They speak fluently about startup evaluation criteria such as TAM, technical defensibility, speed to scale, and AI differentiation, and appear especially motivated by building solutions end-to-end in startup or venture studio environments.”
Mid-level Full-Stack Engineer specializing in AI and FinTech platforms
“Full-stack engineer who built RegArt’s product from 0→1 for enterprise compliance users at clients like HSBC and EY, including the production React frontend, backend APIs, and an LLM-powered search experience. Particularly compelling for startups needing someone who can move across UI, API, and data layers, make pragmatic architecture tradeoffs, and ship fast without over-engineering.”
Mid-level Software Engineer specializing in distributed systems and cloud-based full-stack development
“Software engineering candidate who built a compiler-like Python tool to translate between Python code and UML-style diagrams (and back). Also has hands-on AWS experience building a distributed pub/sub system using services like Lambda, API Gateway, ELB, WAF, VPC, and DynamoDB, plus ML projects using Kaggle datasets (e.g., diabetes risk analysis).”
Mid-Level Software Engineer specializing in distributed microservices and real-time systems
“Software engineer with production experience at DraftKings and SRC, owning high-impact platform changes like early-start lineup validation fixes and a multi-service refactor to support dual-role players (e.g., Ohtani) using backward-compatible, feature-flagged rollouts. Has embedded onsite with military users to rapidly ship improvements to a COP/TAK mapping integration (TrackSync), and leverages AI tools (Claude) to accelerate learning and delivery in new domains (e.g., ESP32 smart deadbolt project).”
Intern Full-Stack/ML Engineer specializing in LLM applications and mobile development
“Backend engineer who built a serverless AWS Lambda microservices backend for a parenting assistance mobile app, including a personalized recommendation system optimized to sub-500ms via precomputed scoring and DynamoDB caching. Demonstrates strong production pragmatism: CloudWatch-driven performance tuning (provisioned concurrency), zero-downtime phased schema migrations, and robustness patterns like optimistic locking and request deduplication. Also led a refactor of an LLM RAG pipeline to improve retrieval quality and cut latency from ~5s to ~3s.”