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AI Systems Programming – Backend, API & AI Engine

Dedicated backend and API for your AI system. Microservices architecture, AI model integration, scaling to millions of requests, and readiness for future extensions.

SERVICE DETAILS

I design and build backends for AI systems — the infrastructure behind a working product. Typical projects: REST/GraphQL APIs integrating multiple AI models (routing, fallback, cost management), task queue systems with Celery/Bull and Redis, real-time data processing pipelines, microservices architecture with Docker and Kubernetes, or serverless on AWS Lambda/Google Cloud Run. AI systems programming is not just calling the OpenAI API — it is caching, rate limiting, token cost logging, retries with exponential backoff, and latency monitoring for every model.

> INVESTMENT:

from €3,500
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Key Benefits

Scalable AI backend ready for millions of requests — designed for growth, not rebuilt every time you hit the next traffic threshold.

Multi-model AI integration with routing and fallback — when GPT-4o is unavailable or too expensive, the system automatically switches to Claude or Gemini.

LLM response caching with Redis — reduces token costs by 30-60% for repeated queries without degrading answer quality.

Cost and latency monitoring for every AI model — you know exactly what you pay per call and can optimise model selection for ROI.

API documentation (OpenAPI/Swagger) and integration tests — your frontend developers and integration partners connect without asking about every endpoint.

The Process

1

Technical requirements and architecture

I define requirements: expected request volume, latency SLA, AI models, data privacy constraints. I design the system architecture — from database schema to the choice between serverless and containers.

2

Core API and AI model integration

Building the backend foundation: API endpoints, AI model routing logic, authentication system, and API key management. Monitoring (Prometheus + Grafana or Datadog) configured from day one.

3

Performance and cost optimisation

I implement caching, task queuing, rate limiting, and fallback strategies. Load testing (k6 or Locust) and optimisation until the target SLA is met.

4

Deploy, CI/CD, and documentation

Configuring the CI/CD pipeline (GitHub Actions or GitLab CI), deployment to the target infrastructure (AWS, GCP, Hetzner), API documentation, and an operational runbook for your ops team.

Frequently Asked Questions

How does an AI backend differ from a standard backend?

AI systems programming requires handling AI model specifics: variable latency (100ms–30s), streaming responses, token cost management, semantic caching, and multi-provider fallback. A different set of challenges from typical CRUD APIs.

Which cloud providers do you support?

AWS (Lambda, EC2, ECS, RDS), Google Cloud (Cloud Run, GKE, BigQuery), Hetzner (VPS for cost-conscious projects), and Vercel/Render for smaller deployments. The choice depends on project requirements and operational budget.

Can I swap AI models without changing the application code?

That is one goal of a well-designed architecture — an abstraction layer over model APIs so that swapping GPT-4 for Claude or Gemini requires no changes to business logic. Model configuration lives in ENV variables or an admin panel.

How do you price backend projects?

Based on complexity: number of endpoints, external integrations, performance requirements, and test coverage level. A simple backend API for an existing application starts at €3,500. Complex microservices systems with multiple AI models and full monitoring — from €8,000.

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BIAŁYSTOK, PL
+48 732 022 086pawel.wiszniewski95@gmail.com