How I Handle Backend Integrations, Performance and Deployment

Python and OpenCV: taking image processing into deployment

I built a Python and OpenCV face-swap system and automated deployment through an AWS EC2 CI/CD pipeline. The work connected computer-vision functionality with a repeatable delivery process. Python implementation and deployment automation were both part of my contribution.

The outcome was a deployed image-processing system supported by an automated release workflow. Python and FastAPI are part of my backend toolkit; this particular project is documented as Python and OpenCV work. I keep project technology descriptions tied to the implementation recorded for that project.

SQL and NoSQL: reducing latency and maintenance overhead

At Summit Crew, I optimized database operations across SQL and NoSQL applications, reducing query latency. I also refactored legacy handlers to improve the architecture and reduce maintenance overhead. My work covered Node.js applications, MySQL, MongoDB, APIs and CRM components.

This is the performance work I can describe directly: improvements to database operations and existing application structure. The outcome was faster queries and less maintenance overhead across core applications. Those changes supported the business features being delivered in the same systems.

Stripe and Nylas: implementing business integrations

I designed and maintained Stripe subscription functionality for plans, invoices, the customer billing portal, webhooks and trial or usage enforcement. On Snootme, I integrated calendar and scheduling capabilities alongside communication features. These integrations connected application behavior with external services.

I also worked on tests covering Stripe webhooks and engine routing. My contribution therefore included both implementing integration behavior and testing important backend paths. The resulting capabilities supported subscription operations and automated scheduling.

Monitoring and delivery: supporting the system in production

I configured observability using Datadog, Prometheus, Grafana and OpenTelemetry to track system performance, latency and health. At Shyft Digitally, I managed AWS CI/CD and deployed NestJS APIs for community tools. My toolkit also includes Docker, FastAPI and cloud services.

This production work complemented feature delivery. It provided visibility into system behavior and repeatable deployment workflows. Together with the database and integration work, it shows how I handle backend responsibilities from implementation through performance optimization and operational support.

Skills relevant to this role

Python, FastAPI, Django, Flask, REST APIs, PostgreSQL, MongoDB, Docker, AWS and CI/CD.

Role interests: Python backend engineering, FastAPI API development, data integration and AI backend services.

Backend skills and integration experience

Selected project experience

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My tools for this work

LLMOps, evaluation and model serving

RAGAS, Langfuse, Arize Phoenix, LangSmith, Instructor, DSPy, NeMo Guardrails, vLLM, Ollama, Structured outputs, LLM-as-judge evaluation.

Backend systems, data and distributed work

Python, FastAPI, Django, Flask, Node.js, NestJS, Express, PostgreSQL, MySQL, MongoDB, Supabase, Redis, DynamoDB, Queues, Background workers, REST APIs.

Cloud infrastructure and alerting

Kubernetes, Docker, AWS EC2, AWS ECS, AWS S3, AWS RDS, AWS Bedrock, GCP, GitHub Actions, CI/CD, OpenTelemetry, Datadog, Prometheus, Grafana, Alerting systems.

Workflow automation and business integrations

n8n, Make, Zapier, Outlook, Nylas, Stripe, Webhooks, CRM integration, Google Analytics API.

Explore my full skills and tools directory