Strong Middle/Senior Back-End Developer (Python)
What Do We Do?
Madgicx is transforming how eCommerce brands scale their digital advertising through AI-powered automation and intelligent optimization. Our platform empowers thousands of brands to make smarter marketing decisions, automate complex workflows, and achieve unprecedented ROAS. We’re building the future of autonomous advertising, where AI and human creativity combine to unlock exponential growth.
Why Madgicx Needs You
As a Backend Engineer at Madgicx, you are responsible for building and maintaining the core APIs and services that power our AI-driven advertising platform. You will design resilient, high-performance backend systems that handle millions of advertising operations daily, integrate with multiple advertising platforms, and serve as the foundation for our AI agent ecosystem.
This is a hands-on engineering role where you’ll ship production code daily, collaborate closely with AI/ML teams, and directly impact thousands of brands’ advertising performance.
What You’ll Own
1. Core API Development
Build and maintain RESTful APIs serving web, mobile, and AI agent clients
Design database schemas and optimize query performance for high-traffic endpoints
Implement authentication, authorization, and rate limiting for secure API access
Write comprehensive tests (unit, integration, E2E) ensuring code quality and reliability
2. Service Integration & Data Pipelines
Integrate with advertising platform APIs (Meta Marketing API, Google Ads API, TikTok Ads)
Build data ingestion pipelines processing campaign metrics, creative performance, and audience insights
Handle webhook processing, event streaming, and asynchronous job processing
Implement retry logic, circuit breakers, and graceful degradation for external service failures
3. AI-Ready Infrastructure
Build backend services that support AI agent workflows and LLM integrations
Design APIs that enable AI agents to query data, execute actions, and receive feedback
Implement observability hooks (tracing, logging, metrics) for AI system monitoring
Collaborate with Data Science teams to productionize ML models and predictions
4. Performance & Scalability
Optimize API response times (P95 <500ms) and database query performance
Implement caching strategies (Redis, CDN) for high-traffic endpoints
Design for horizontal scalability using containerization and orchestration
Monitor system health, troubleshoot production issues, and participate in on-call rotation
5. Code Quality & Collaboration
Write clean, well-documented, and maintainable Python code following team standards
Conduct thorough code reviews providing constructive feedback to peers
Participate in pair programming sessions and knowledge-sharing activities
Contribute to technical documentation, runbooks, and architectural decision records
What You’ll Bring
Required
Bachelor’s degree in Computer Science, Software Engineering, or equivalent experience
5+ years of production backend development with demonstrable impact on system reliability and performance
Python expertise: Strong proficiency in Python web frameworks (FastAPI, Flask, Django) and async programming
API design: Experience building RESTful APIs with proper versioning, error handling, and documentation (OpenAPI/Swagger)
Database proficiency: SQL expertise (PostgreSQL, MySQL) including schema design, indexing, and query optimization
Cloud infrastructure: Hands-on experience with Google Cloud Platform (preferred) or AWS/Azure
GenAI & Agentic Framework familiarity: Practical experience using GenAI tools (GitHub Copilot, ChatGPT, Claude) for development, understanding of LLM capabilities/limitations, worked on setting up agentic toolings such as RAG, Guardrails, Feedback loops, planning, MCP’s and orchestration tools etc...
Containerization: Production experience with Docker and Kubernetes for service deployment
Non-Negotiable Technical Skills
Backend Core
Python Web Frameworks (FastAPI, Flask, or Django with async capabilities), SQL Database Design (PostgreSQL schema design, indexing strategies, query optimization), and API Architecture (RESTful design, authentication/authorization, rate limiting, error handling)
Cloud & Infrastructure
Google Cloud Platform (Compute Engine, Cloud Run, Cloud SQL, Pub/Sub, Secret Manager, IAM), Container Orchestration (Docker containerization, Kubernetes deployments, service configuration), and Infrastructure as Code (Terraform or equivalent for reproducible deployments)
Integration & Data
Third-Party API Integration (OAuth flows, webhook processing, rate limit handling, retry logic), Message Queuing (Pub/Sub, RabbitMQ, or Kafka for async processing), and Caching Strategies (Redis for session management, query results, and rate limiting)
GenAI & Observability
GenAI Tool Proficiency (Active use of GitHub Copilot, ChatGPT, or Claude for code generation, debugging, and documentation), LLM Integration Basics (Understanding of API usage, token management, prompt engineering, etc... ), and Observability (Structured logging, distributed tracing, metrics collection, and alerting)
Our Operating Principles
Ship Code, Not Slides: We measure success by features in production, not meeting decks
Engineering Excellence: Clean code, tested systems, thorough documentation, and scalable architectures
Speed to Production: From feature branch to deployment in days, not weeks
Data-Driven Decisions: We trust metrics, experiments, and user feedback over opinions
Continuous Learning: Every production incident and performance issue teaches us something new
What Success Looks Like (First 12 Months)
Shipping velocity: Consistently deliver features from design to production within sprint cycles
System reliability: Maintain <0.1% API error rate and P95 response times under target SLAs
Code quality: Your PRs require minimal revisions and serve as examples for junior engineers
AI readiness: Backend services seamlessly support AI agent integrations and ML model deployments
Team impact: Improve developer productivity through better tooling, documentation, or architectural patterns
What’s In It For You
Impact: Your APIs power advertising decisions affecting billions in ad spend across thousands of brands
Technical Growth: Work with cutting-edge AI/ML systems, high-traffic APIs, and modern cloud infrastructure
Ownership: Full responsibility for services from design through production operation
Learning Budget: Conferences, courses, certifications, and dedicated learning time
Compensation: Competitive salary, equity, performance bonuses, and comprehensive benefits
Flexibility: Remote-first culture with flexible hours and work-life balance
Ready to Build the Future of Advertising AI? If you’re excited by the challenge of building ML systems that generate millions in revenue, we want to talk. Send us your GitHub, examples of production platforms you’ve built, and evidence of business impact you’ve driven. Show us why you’re the engineer who will help us revolutionize advertising through AI.