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Vetted NoSQL Professionals
Pre-screened and vetted.
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
Mid-level Full-Stack Engineer specializing in SaaS and product development
Senior Full-Stack Engineer specializing in cloud architecture and AI/ML integration
Senior Software Engineer specializing in cloud-native full-stack systems
Mid-Level Full-Stack Software Engineer specializing in Java/Spring Boot microservices
Mid-level Backend Engineer specializing in distributed systems and FinTech payments
Mid-level Software Developer specializing in cloud microservices and full-stack platforms
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 Developer specializing in full-stack and backend systems
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
Mid-level Data Engineer specializing in GCP, Spark, and healthcare analytics
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.”
“Built and deployed a production Retrieval-Augmented Generation (RAG) platform in a healthcare setting to automate clinical documentation review and summarization, targeting near-real-time, explainable outputs. Emphasizes grounded generation to reduce hallucinations, latency optimizations (chunking/embedding reuse), and PHI-safe workflows with access controls, plus strong orchestration experience using Apache Airflow.”
Sasi Katamneni
Mid-level Data Scientist / AI-ML Engineer specializing in Generative AI and LLM applications
“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”