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Vetted Amazon Kinesis Professionals
Pre-screened and vetted.
Principal Backend Software Engineer specializing in JVM microservices on AWS
Mid-level Machine Learning/AI Engineer specializing in GenAI, RAG, and LLM inference
Mid-level Software Engineer specializing in backend systems, cloud platforms, and AI services
Mid-level Full-Stack Java Developer specializing in cloud-native microservices
Mid-level Data Scientist specializing in NLP, risk analytics, and MLOps
Senior DevOps/Cloud Engineer specializing in multi-cloud infrastructure and CI/CD automation
Senior Full-Stack Developer specializing in Java microservices and AWS cloud
Mid-Level Data Engineer specializing in cloud data pipelines and lakehouse/warehouse platforms
Senior Full-Stack Software Engineer specializing in cloud-native microservices and event-driven systems
Mid-level Data Engineer specializing in cloud lakehouse and streaming analytics for financial services
Mid-level Data Engineer specializing in cloud data pipelines and analytics (AWS/Azure)
Mid-level Data Engineer specializing in cloud ETL, streaming, and ML-ready data pipelines
Mid-level AI/ML Engineer specializing in cloud MLOps, LLM agents, and risk & fraud modeling
Senior Data Scientist specializing in ML engineering and cloud analytics
Senior Software Engineer specializing in distributed systems and full-stack development
Manoj Cooray
Staff Software Developer specializing in enterprise backend and event-driven systems
“Backend-heavy engineer with deep experience building enterprise and real-time systems across healthcare, operations monitoring, e-commerce, and 911 call center domains. He has led and personally coded greenfield and customer-facing platforms, including cloud/on-prem integrations, custom workflow tooling, and microservices architectures, while now independently upskilling into modern TypeScript/React-based frontend technologies.”
Nagaraju Kanubuddi
Mid-level AI/ML Engineer specializing in fraud detection, recommender systems, and forecasting
“ML engineer/data scientist who built and deployed a real-time fraud detection platform at Citi on AWS SageMaker, processing 3M+ daily transactions and improving fraud response by 28%. Combines unsupervised anomaly detection (autoencoders) with ensemble models (XGBoost/Random Forest) plus Airflow/Step Functions orchestration, drift monitoring, and explainability (SHAP) to keep models reliable and compliant in production.”
Divya Ganapala
Mid-level Data Scientist specializing in cloud ML, MLOps, and predictive analytics
“NLP/ML engineer with hands-on healthcare and support-ticket text experience, building clinical-note structuring and semantic linking systems using spaCy, BERT clinical embeddings, and FAISS. Emphasizes production-grade delivery (Airflow/Databricks, PySpark, Docker, AWS/FastAPI/Lambda) and rigorous validation via clinician-labeled datasets, retrieval metrics, and user feedback.”