Develop secure, scalable, and high-performance backend architectures.
Implement asynchronous processing, event-driven communication, and API integrations.
Collaborate with frontend, mobile, DevOps, product, and AI engineering teams.
Support cloud-native deployments using containers and Kubernetes.
Write clean, maintainable, and testable code with proper documentation and testing practices.
Participate in code reviews, debugging, monitoring, and production support activities.
Contribute to CI/CD pipelines, automated testing, and engineering best practices.
Implement logging and monitoring for AI workflows including request tracing, latency tracking, and error analysis.
Qualifications
Need to Have
5+ years of experience in backend or platform engineering.
Strong hands-on expertise in Python and FastAPI development.
Experience building REST APIs, microservices, and backend integration services.
Experience working with Generative AI, LLM APIs, AI orchestration frameworks, or AI-powered applications.
Strong understanding of distributed systems and cloud-native application development.
Experience with databases such as PostgreSQL, MongoDB, Redis, or vector databases.
Knowledge of asynchronous programming and event-driven architectures.
Experience with Docker, Kubernetes, CI/CD pipelines, and cloud platforms such as AWS, Azure, or GCP.
Strong understanding of API security, authentication, and authorization mechanisms.
Familiarity with Git-based workflows and Agile development practices.
Strong debugging, problem-solving, and communication skills.
Experience with RAG pipelines, vector databases, and semantic search.
Good to Have
Exposure to LangChain, LlamaIndex, OpenAI APIs, or similar AI frameworks.
Experience with Kafka, RabbitMQ, or messaging/event-streaming platforms.
Familiarity with Flutter/mobile integrations and frontend-backend communication.
Experience in QSR, retail, hospitality, or enterprise digital platforms.
Exposure to workflow automation and enterprise orchestration systems.
Experience with observability, monitoring, and logging tools.
Understanding of AI governance, prompt engineering, and AI evaluation frameworks.
