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Vetted Amazon DynamoDB Professionals
Pre-screened and vetted.
Mid-level Full-Stack Software Engineer specializing in AI platforms and LLM applications
Mid-level Python Backend Developer specializing in FinTech and ML-driven fraud detection
Senior Data/GenAI Engineer specializing in cloud-native ML, RAG, and real-time data platforms
Mid-Level Software Development Engineer specializing in AWS serverless and backend APIs
Mid-level Software Engineer specializing in cloud infrastructure and distributed systems
Mid-level Software Engineer specializing in backend systems, microservices, and AI search
Senior Full-Stack Engineer specializing in AI-powered enterprise applications
Senior Full-Stack Engineer specializing in cloud-native web and mobile platforms
Senior Full-Stack Engineer specializing in data-driven SaaS and web platforms
Principal Software Engineer specializing in platform engineering and distributed systems
Executive Engineering Leader specializing in cloud platforms, infrastructure, and SRE
Principal Software Engineer specializing in distributed systems and cloud microservices
Nehal Mahankali
Mid-level Full-Stack Python Developer specializing in cloud-native banking applications
“Backend engineer who built a low-latency real-time transaction API in Python/Flask, with strong depth in PostgreSQL/SQLAlchemy performance tuning (time-based partitioning, indexing, connection pooling). Has production experience integrating ML scoring and OpenAI-style APIs with safety/latency controls, and designing multi-tenant isolation strategies including per-tenant pooling/caching and premium-tenant isolation.”
Aishwarya Sheelvant
Junior Backend & Data Engineer specializing in cloud infrastructure and ML pipelines
“Built a GenAI/RAG-based ESG questionnaire-answering agent at C3.ai, including a React dashboard with role-based access and human-in-the-loop verification by showing supporting source paragraphs. Reported outcomes included cutting a 4–5 week manual process down to about a week (~90% labor reduction) and a client-reported ESG rank improvement from 7th to 3rd.”
Xinyuan Lin
Intern Software Engineer specializing in LLMs, RAG, and full-stack systems
“Built and productionized a multi-agent LLM analytics assistant at eBay that routes natural-language questions to retrieval or text-to-SQL, dynamically retrieves relevant schemas via a vector DB, and executes against a data warehouse. Drove a major quality lift (text-to-SQL accuracy 60%→85%) and materially reduced time engineers/PMs spent getting data insights through strong eval/monitoring, tracing, and reliability-focused design (schema retrieval, strict JSON outputs, retries/clarifications).”