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Vetted Amazon DynamoDB Professionals
Pre-screened and vetted.
Senior Full-Stack Engineer specializing in telehealth and commerce platforms
Senior Software Engineer specializing in backend platforms and data systems
Mid-Level Full-Stack Software Developer specializing in AWS cloud and automation
Junior Software Engineer specializing in distributed systems and streaming platforms
Senior Software Engineer specializing in backend distributed systems and FinTech
Mid-Level Full-Stack Software Engineer specializing in cloud-native systems and FinTech
Mid-level Software Engineer specializing in backend systems, billing, and real-time data pipelines
Intern Full-Stack Software Engineer specializing in distributed systems and cloud services
Principal Data Scientist / AI Engineer specializing in healthcare-native AI platforms
Executive engineering leader specializing in cloud platforms, APIs, and FinTech
Senior Software Engineer specializing in generative AI and real-time platforms
Senior Full-Stack Engineer specializing in AI, cloud, and enterprise platforms
Rhutwij Tulankar
Engineering Manager and ML/Data Architect specializing in scalable data platforms and personalization
“Hands-on engineering manager at a marketing company leading a highly senior, distributed team (10 direct reports) while personally coding ~60–70% and owning end-to-end architecture across three interconnected products. Built agentic CRM automation and a reinforcement-learning-driven distribution layer for channel spend/bidding, with a strong focus on scalable design and observability (Prometheus/APM/logging) enabling frequent releases and few production incidents.”
Vigynesh Bhatt
Mid-level Software Engineer specializing in backend, cloud, and ML systems
“Software engineer with experience across Goldman Sachs, BYU Broadcasting, Juniper Networks, and an edtech startup (Doubtnut), spanning data migrations, AWS-based media backends, and microservices observability. Built a Redis/ElastiCache caching layer in front of DynamoDB/S3 to improve media delivery latency and cost, and created an SEO indexing automation tool using the Google Search Console API that saved ~15–30 person-hours per day.”
Matthew Allyn
Executive engineering leader specializing in enterprise architecture, cloud, AI, and FinTech
“Engineering leader with experience modernizing e-commerce platforms and leading product strategy for data-driven franchise growth tools. Notable for scaling distributed teams, making pragmatic build-vs-buy and architecture decisions, and delivering measurable business impact including dramatically faster checkout performance and revenue gains.”
Alexander Choy
Director of AI/ML Engineering specializing in MLOps, data platforms, and 3D computer vision
“Backend/data engineer focused on production ML/LLM systems: built a real-time FastAPI inference API on Kubernetes with strong reliability patterns (timeouts, idempotent retries, centralized error handling). Delivered AWS platforms using EKS + Lambda with GitHub Actions/Helm CI/CD and built Glue-based ETL from S3/Kafka into Snowflake with schema evolution and data-quality controls; also modernized legacy analytics/recommendation workflows into Python services with safe, feature-flagged cutovers.”
Yashvi Shah
Mid-Level Software Engineer specializing in distributed systems and cloud platforms
“Amazon Alexa engineer who architected and shipped a GenAI Knowledge Agent used by 2M+ customers, focused on making LLM outputs auditable via citations and a verification layer that prevents hallucinations. Built the full vertical slice (FastAPI/LangChain backend + React/TypeScript streaming UI) while keeping p99 latency under 200ms, and has proven incident response experience on AWS (Lambda/DynamoDB scaling issues).”
Ashi Sinha
Junior Software Engineer specializing in full-stack and ML/NLP systems
“Entry-level full-stack engineer with internship experience at Amazon (Appstore IAP flow + uninstall recommendation workflow) and a health-tech startup (OneVector) where they built a DSUR reporting workflow end-to-end, including document generation, S3-backed versioning/metadata, and secure preview/download. Demonstrates strong production debugging and reliability mindset (instrumentation, deterministic retrieval, idempotent writes) and focuses on UX/performance in high-stakes user flows.”