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Vetted NoSQL Professionals
Pre-screened and vetted.
Mid-Level Software Development Engineer specializing in cloud-native backend systems
Mid-level Machine Learning Engineer specializing in MLOps and applied AI
Mid-Level Full-Stack Software Engineer specializing in microservices and cloud
Mid-level DevOps & MLOps Engineer specializing in cloud-native CI/CD and Kubernetes
Senior Software Engineer specializing in backend microservices and AI/ML integrations
Junior ML Engineer specializing in GenAI agents, RAG, and computer vision
Mid-level Machine Learning & Generative AI Engineer specializing in enterprise RAG and MLOps
Senior Full-Stack Python Engineer specializing in cloud microservices and MLOps
Mid-level AI/ML Engineer specializing in NLP, MLOps, and compliance-focused ML systems
Senior Full-Stack Engineer specializing in cloud architecture and AI/ML integration
Mid-Level Full-Stack Software Engineer specializing in Java/Spring Boot microservices
Mid-level Full-Stack Engineer specializing in Python, FastAPI, and cloud-native systems
Senior Cloud Security Engineer specializing in AWS and Azure security architecture
Mid-level Software Engineer specializing in AI, data engineering, and cloud systems
Senior QA Engineer specializing in automation, data quality, and cross-platform testing
Mid-level SDET / QA Automation Engineer specializing in API and UI test automation
Junior Full-Stack/Cloud Engineer specializing in AI and data-driven applications
Jainum Sanghavi
Mid-level DevOps Engineer specializing in cloud automation and Kubernetes platforms
“Robotics/ML engineer who has built SO(3)-equivariant models for robotic manipulation, including custom equivariant layers and differentiable point-cloud rasterization/derasterization workflows. Also brings 2 years of DevOps experience in banking systems, automating CI/CD and infrastructure at scale (managed 180 OCI servers; reduced rebuild downtime by 80%).”
Caden Cheah
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.”