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DJANGO · FLASK · CELERY · POSTGRESQL

Backend Platforms (Django/Flask)

Scalable APIs, jobs, caching, observability.

01 / Core Capabilities

Celery/RedisPostgreSQLAWS

02 / Execution Pipeline

  1. 01
    Discovery

    Clarify architectural goals, constraints, and target SLAs.

  2. 02
    Architecture

    Design schemas, interfaces, and safety guardrails.

  3. 03
    Build

    Implement production modules with clean abstractions.

  4. 04
    Optimize

    Validate with automated test harnesses and evaluations.

  5. 05
    Deploy

    Deploy with CI/CD, observability, and zero-downtime rollout.

03 / Technical FAQs

01Do you support microservices or monoliths?+
Both, pragmatic architectures based on team size, scaling needs, and delivery velocity.
02What does your stack look like?+
Python (Django/Flask), Postgres/Redis, Celery, Docker, and AWS services for reliability.
03Can you improve performance of existing systems?+
Yes, profiling, caching, query tuning, and background jobs are standard engagements.
04Do you add monitoring and tracing?+
Yes, metrics, logs, and traces with alerts for fast incident response.
05How do you handle authentication and authorization?+
JWT/OAuth2, session-based auth, RBAC/ABAC, and secure secrets handling.
06What about background jobs and scheduling?+
Celery/Redis for queues, periodic tasks, retries with backoff, and idempotency keys.
07Do you set up CI/CD and multi-environments?+
GitHub Actions, environment parity, blue/green or rolling deploys, and rollback plans.
08How do you ensure database reliability?+
Migrations, backups, read replicas, and query optimization with slow query observability.