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Vetted AWS CodePipeline Professionals
Pre-screened and vetted.
Mid-Level Software Engineer specializing in backend, cloud, and GenAI assistants
Senior Python/ML Engineer specializing in LLM-powered backend systems
Staff Software Engineer specializing in payments platforms and event-driven microservices
Senior Full-Stack (MERN) Developer specializing in e-commerce and FinTech platforms
Mid-level Backend/Distributed Systems Engineer specializing in cloud-native data pipelines
Senior Cloud & Backend Engineer specializing in AWS and DevOps automation
Mid-level Cloud DevOps Engineer specializing in AWS/Azure, Kubernetes, and IaC
Senior Multi-Cloud DevOps/SRE Engineer specializing in Azure, AWS and IaC
Mid-level Data Engineer specializing in cloud data pipelines and analytics platforms
Mid-level Software Engineer specializing in full-stack cloud and embedded systems
Mid-level Full-Stack Developer specializing in Java/Spring Boot, React, and cloud microservices
Mid-level AI/ML Engineer specializing in GenAI, RAG, and cloud-native ML platforms
Mid-level Full-Stack Developer specializing in .NET, Python/Django, and cloud-native web apps
Mid-Level Software Engineer specializing in cloud backend and GenAI assistants
Mid-level AI/ML Engineer specializing in NLP, RAG, and agentic AI
Senior Software & Data Engineer specializing in cloud platforms and streaming data
Mid-Level Software Engineer specializing in cloud-native distributed systems
Keerthana Chinthalapally
Senior Full-Stack Developer specializing in cloud-native microservices
“Bank of America engineer/product owner who built a real-time transaction insights and spending categorization platform using React/TypeScript and Spring Boot microservices with Kafka. Deep experience in event-driven architectures, performance tuning at peak banking loads, and reliability patterns (SLOs, observability, feature flags, DLQs). Also created an internal monitoring/alerting tool adopted across engineering and ops, cutting incident response time by 40%+.”
David Kidwell
Senior AI/ML Data Scientist specializing in NLP, computer vision, and MLOps
“Applied LLMs and a graph-RAG architecture in Neo4j to automate an accounting firm's cross-checking of transactional books against tax regulations, indexing 1,000+ pages into a knowledge graph with vector search. Combines agentic LLM workflows with classical NER (Hugging Face/NLTK) and validates using expert-labeled held-out data plus precision/recall and measured accountant time savings after deployment.”