Designing event-driven telemetry pipelines, structured data platforms, and inference-ready backend infrastructure on AWS
Years Engineering
Serverless Architectures
Event Pipelines
Data Infrastructure
Builds distributed telemetry ingestion systems, structured event pipelines, and analytics infrastructure supporting downstream machine-learning workflows and inference services on AWS.
Designing services converting live device traffic into structured datasets supporting analytics and downstream ML workflows.
Building APIs and storage layers enabling low-latency access to structured features and prediction-ready signals.
Delivering distributed processing pipelines using AWS Lambda, Step Functions, DynamoDB and S3.
Introducing structured logging, monitoring and alerting across asynchronous microservice architectures.
Containerised SIP traffic listener reconstructing fragmented TCP streams via libpcap and producing ML-ready structured event datasets.
Asynchronous microservices processing operational telemetry streams into DynamoDB and S3 datasets supporting monitoring and downstream decision systems.
Lambda and API Gateway services exposing structured datasets to analytics, automation workflows and ML-ready downstream consumers.
Example architecture illustrating telemetry ingestion, feature pipelines, training workflows and inference-ready service layers.