Pre-screened and vetted.
Senior Full-Stack Engineer specializing in AI/ML platforms and cloud-native systems
Senior Full-Stack Engineer specializing in cloud-native SaaS and AI applications
“ML/LLM engineer with hands-on experience shipping production RAG systems at Google Clinical Search and GenAI recommendation/summarization features in Meta Ads. Stands out for combining research-to-production execution with rigorous grounding, evaluation, safety checks, and reusable Python platform components that improved both reliability and team velocity.”
Executive Technology Leader specializing in distributed systems, cloud infrastructure, and AI/ML
“Engineering leader/player-coach who built and validated a preference-based recommendation engine using clustering, including generating test data to evaluate how clusters evolve over time. Has SRE/DevOps experience and has owned production incidents end-to-end (logging-driven RCA and refactoring patterns that failed at large data scale). Emphasizes quality and platform stability via unit, integration, and load testing, and has managed performance via regular 1:1s and PIPs.”
Senior Software Engineer specializing in distributed systems and high-scale backend platforms
Senior Full-Stack Software Engineer specializing in AI platforms and cloud data systems
Staff Full-Stack Software Engineer specializing in cloud platforms and real-time health data
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Staff Software Engineer specializing in ML infrastructure and data platforms
Senior Machine Learning Engineer & Solution Architect specializing in cloud AI systems
“Backend/ML platform engineer with Google experience leading Python microservices for an AI-driven recommendation/retrieval system, including PyTorch inference and a retrieval-augmented generation workflow. Strong in production Kubernetes + GitOps (ArgoCD), real-time Kafka/Spark pipelines, and phased on-prem/legacy to AWS/GCP cloud migrations with reliability-focused rollout and rollback practices.”
Staff Machine Learning Scientist specializing in NLP, LLMs, and Generative AI
Senior Data Engineer specializing in cloud data platforms and real-time streaming
Staff Full-Stack Software Engineer specializing in cloud platforms and healthcare data pipelines
Senior Full-Stack Engineer specializing in AI and GenAI platforms
Staff Software Engineer specializing in distributed systems, cloud platforms, and AI services
“Meta engineer who owned end-to-end production systems for AI-enabled smart glasses, spanning React/TypeScript apps through Node/Java microservices on AWS EKS with Kafka/Postgres. Built and productionized a real-time RAG pipeline (LangChain + OpenAI + Elasticsearch) with rigorous guardrails (shadow/canary, fallbacks, monitoring), delivering major improvements in latency (~35–40%), error reduction (~30%), and engagement (reported +40% DAU).”
Mid-level Software Engineer specializing in backend systems, real-time data pipelines, and FinTech
“Backend/platform engineer who has owned real-time reporting and streaming analytics systems end-to-end, combining FastAPI/Postgres APIs with Kafka consumers, Celery background jobs, and Redis caching. Strong DevOps/GitOps experience deploying Python/Node microservices to AWS EKS with Helm, ArgoCD/FluxCD, and CI pipelines, and has supported phased on-prem to AWS migrations using Terraform and traffic cutovers.”
Senior Full-Stack Engineer specializing in SaaS, e-commerce, and frontend platforms
“Frontend-leaning full-stack engineer who has built a multi-tenant AI-powered widget and admin dashboard platform used across 40+ merchant websites. Strong in TypeScript/Next.js/GraphQL systems design, reusable platform primitives, and cross-layer debugging, with a clear track record of shipping scalable product experiences under ambiguity.”
Senior Software Engineer specializing in cloud infrastructure and distributed systems
“Amazon engineer focused on productionizing LLM-powered developer workflows, including code assistance, debugging automation, and internal AI tooling. Stands out for combining hands-on ML systems work with strong platform engineering, including an orchestration engine that reportedly saved about $10K/day and reduced a manual workflow from 12 hours to under a second.”
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference