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Vetted Amazon Kinesis Professionals
Pre-screened and vetted.
Mid-level Cloud & DevOps Engineer specializing in AWS/Azure and Kubernetes
Mid-level Data Engineer specializing in cloud ETL, streaming, and data warehousing
Senior Software Engineer specializing in backend, cloud platforms, and AI/ML
Principal Full-Stack Engineer specializing in cloud-native platforms and FinTech
Shamala Chandrappa
Mid-level AI Engineer specializing in agentic AI, LLM systems, and healthcare AI
“Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.”
Principal Software Architect specializing in Healthcare IT and cloud-native systems
Senior Product Manager / Project Manager specializing in data platforms, BI, and cloud transformation
Senior Backend Python Engineer specializing in cloud-native APIs and data platforms
Senior Founding Engineer specializing in AI/LLM and serverless systems
Principal Cloud & Data Architect specializing in AI-enabled AWS platforms
Mid-level Full-Stack Software Engineer specializing in cloud backends and applied AI
Deborah Gaynor
Senior Software Engineer specializing in full-stack backend systems and integrations
“Full-stack engineer with startup experience at sub-100-person companies, owning major product and platform changes across Go, React/TypeScript, Python, Postgres, and SQL Server. Stands out for redesigning a core repository-like workflow component without breaking existing customer data and for shipping operational tooling at Tovala that saved teams 15 hours of manual work per week.”
bharath burgoju
Mid-Level Data Engineer specializing in cloud data pipelines and big data platforms
“Data engineer with ~4 years of experience building Python-based data ingestion/processing services and real-time streaming pipelines (Kafka/PubSub + Spark Structured Streaming). Has deployed containerized data applications on Kubernetes with GitLab CI/Jenkins pipelines and applied GitOps to cut deployment time ~40% while reducing config drift. Also supported a legacy on-prem data warehouse/backend migration to GCP using phased migration and parallel validation to meet strict reliability/SLA needs.”
Mid-level Data Scientist specializing in ML, data engineering, and real-time analytics
Mid-level AI/ML Data Engineer specializing in secure ML pipelines and AI governance
Mid-level Backend Software Engineer specializing in cloud microservices and distributed systems