Pre-screened and vetted.
Mid-level Data Science AI/ML Engineer specializing in Generative AI, LLMs, and RAG systems
“Built a production RAG-based "knowledge copilot" for support/ops using LangChain/LangGraph, implementing the full pipeline (ingestion, chunking, embeddings, vector DB retrieval/rerank, guarded generation with citations) and operating it as monitored microservices with CI/CD. Also designed an event-driven, streaming backend for real-time inventory ordering predictions that reduced stockouts by 25%, and has hands-on incident response experience stabilizing LLM API latency/5xx spikes using Datadog/APM and resilience patterns.”
Intern Software Engineer specializing in systems, containers, and cloud infrastructure
Mid-level Software Engineer specializing in cloud-native microservices and FinTech
Staff QA & Performance Engineer specializing in mobile, graphics, and AR/VR testing
Mid-Level Software Engineer specializing in AWS serverless and full-stack web development
Executive technology leader specializing in SaaS, cybersecurity, and healthcare IT
Senior QA Analyst specializing in accessibility, mobile, and cross-platform testing
Mid-Level Software Engineer specializing in FinTech and distributed data platforms
Mid-level AI/ML Engineer specializing in RAG systems and cloud data platforms
Mid-Level Software Development Engineer specializing in AWS serverless, security, and ML platforms
Senior Performance & GPU Virtualization Engineer specializing in AI/ML and cloud certification
Senior QA Engineer specializing in AAA game quality engineering and Unreal Engine validation
Staff Software Engineer specializing in enterprise SaaS, AI assistants, and distributed systems
Junior Software Engineer specializing in backend and systems development
Staff-level Software Engineer specializing in Unity real-time and cloud multiplayer systems
Intern Full-Stack Software Engineer specializing in scalable web platforms
Mid-level AI/ML Engineer specializing in recommendation, retrieval, and MLOps
Mid-level AI/ML Engineer specializing in NLP, graph models, and MLOps for FinTech and Healthcare
“AI/ML engineer who has deployed production LLM/transformer-based systems for merchant intelligence and fraud/support optimization, delivering +27% merchant engagement and +18% payment success. Deep experience in privacy-preserving, PCI DSS-compliant data/ML pipelines (Airflow, AWS Glue, Spark, Delta Lake) and scalable microservices on Kubernetes, plus proven cross-functional delivery in healthcare claims analytics at UnitedHealth Group (12% HEDIS claim reduction).”
Mid-Level Full-Stack Software Engineer specializing in Java/Spring, React, and AWS
“Backend/full-stack engineer (5+ years) with Shopify experience integrating LLM/RAG workflows into production APIs. Owned a Python TensorFlow Serving inference pipeline connected to Java microservices via gRPC, optimizing tail latency at ~10k concurrent load and improving retrieval relevance with embedding and evaluation work. Strong Kubernetes/EKS + GitOps/CI/CD background, including monolith-to-microservices migrations and event-driven streaming patterns.”
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.”
Mid-Level Software Engineer specializing in real-time data pipelines and ML deployment
“Ticketmaster data engineer who built CDC-driven Kafka pipelines feeding Snowflake for analytics and data science teams. Hands-on in production operations—scaled Kafka during sudden playoff-driven transaction spikes and improved monitoring for preemptive scaling. Known for using small-batch experiments and quantitative metrics to align stakeholders and drive cost-saving architecture changes (e.g., buffering to reduce AWS Lambda invocation frequency).”