Senior Backend Engineer (Python / AI / Azure)

About the Role

We are looking for a Senior Backend Engineer with strong Python experience and a solid understanding of AI-driven systems. You will be responsible for designing, developing, and optimizing high-performance backend systems that power large-scale AI applications — such as chatbots, vision-based analytics, and cloud-based inference pipelines.

The ideal candidate has experience with parallel computation, cloud scaling (Azure), and system reliability, ensuring our services remain efficient and responsive even under heavy workloads.

Key Responsibilities

• Design & implement scalable backend architectures using Python (FastAPI, Flask, or Django).

• Build and optimize AI-powered backend services, integrating models and inference pipelines with minimal latency.

• Develop and manage cloud infrastructure on Azure (AKS, App Services, Azure Functions, Azure Storage, etc.).

• Implement task orchestration and parallel computing workflows using multithreading, multiprocessing, and asynchronous execution for compute-intensive workloads.

• Design auto-scaling mechanisms to dynamically allocate compute resources (VMs, containers, GPUs) based on workload or active users.

• Integrate and optimize databases (PostgreSQL, Redis, or similar) for data persistence, caching, and fast retrieval.

• Collaborate with AI and FE engineers to deploy and serve machine learning models efficiently (batch or real-time inference).

• Monitor and troubleshoot performance bottlenecks, ensuring uptime, fault tolerance, and system reliability.

• Contribute to DevOps and CI/CD pipelines, automating deployment and testing workflows.

• Mentor junior engineers and review code for best practices and maintainability.

Must Have

• 5+ years of backend development experience, with at least 3 years in Python (FastAPI, Flask, or Django).

• Strong understanding of object-oriented programming and software architecture patterns (e.g., microservices, event-driven systems).

• Hands-on experience with Azure Cloud Services (AKS, Web App, App Service, VM scaling, Azure Blob Storage).

• Experience in parallel and distributed processing (multithreading, multiprocessing, async I/O, Celery, Ray, Dask, etc.).

• Solid experience with auto-scaling infrastructure (compute scaling, load balancing, or container orchestration).

• Experience integrating AI models or services (e.g., chatbot backends, image recognition APIs, or ML inference systems).

• Solid experience with PostgreSQL and query optimization.

• Familiarity with RESTful APIs, WebSocket, and microservice communication patterns.

• Knowledge of Git, Docker, CI/CD pipelines, and unit testing frameworks (pytest, unittest).

Nice to Have

• Familiarity with GPU scheduling or AI inference optimization (ONNX Runtime, TensorRT, etc.).

• Experience with message queues (RabbitMQ, Kafka, Azure Service Bus).

• Experience with infrastructure-as-code (Terraform, Bicep, or ARM templates).

• Exposure to monitoring and logging systems (Prometheus, Grafana, Azure Monitor, ELK).

• Basic understanding of AI/ML lifecycle, including model deployment, serving, and monitoring.