Cloud & AI
Cloud platforms
Amazon Web Services (AWS)
Hands-on with EC2, S3, RDS, IAM, VPC, CloudWatch, Lambda, SNS, and data/analytics services including Glue, Athena, and QuickSight. I’ve built serverless-style pipelines (e.g. S3 → Glue → Athena → QuickSight), automated access with IAM roles and least-privilege policies, and used boto3 for repeatable operational scripts.
Google Cloud Platform (GCP)
Compute Engine, Cloud Storage, BigQuery, IAM, and analytics patterns — plus portable habits from AWS/Azure so workloads stay consistent across clouds.
Firebase
Production use of Firebase Authentication, Firestore, and Cloud Functions behind React (Vite) clients — security rules, modular architecture, and AI-assisted features wired through HTTPS APIs and server-side orchestration.
AI & LLM engineering
OpenAI, LangChain & agents
LangChain pipelines with OpenAI APIs for SQL generation, structured outputs, and workflow automation — the same patterns that power QueryAgent-style tools and internal automations at NGOs and service companies.
Proxy to AI (API gateway pattern)
Backend services expose a stable internal API; a proxy layer holds provider keys, applies rate limits and logging, and lets you change models or vendors without rewriting clients. That’s how I keep keys off devices and keep AI traffic auditable.
Voice & serverless
Alexa skills on AWS Lambda with Python — voice as another client into the same disciplined backend ideas.
DevOps & delivery
Docker, Kubernetes (EKS, AKS), Helm, Terraform, CloudFormation, ARM templates, Azure DevOps, GitHub Actions, GitLab CI, Jenkins, and DevSecOps practices (policy-as-code, least privilege, automated checks).
Observability & security
HashiCorp Vault, Prometheus, Grafana, Splunk, Loki, CloudWatch, IAM hygiene — metrics, logs, and secrets treated as part of the platform, not an afterthought.
Data & BI
SQL, Snowflake, Pandas/NumPy, and visualization with Power BI, Tableau, and QuickSight — so AI and cloud work connect to numbers stakeholders actually use.