Senior Backend Engineer - Platform & Infrastructure Company Description: Lambda Theta specializes in designing and developing AI-powered software solutions for international clients. With expertise in computer vision, natural language processing (NLP), artificial intelligence, and embedded systems, we deliver state-of-the-art, customized solutions that address complex industry challenges. Our focus is on building highly specialized and indigenized software systems tailored to client needs. Key Responsibilities: API Gateway & Load Balancing: Design and implement robust API gateway solutions with load balancing, rate limiting, SSL termination, and DDoS protection Session & Cache Management: Build scalable session management systems with multi-level caching strategies (L1 in-chat, L2 shared, L3 distributed) Task Orchestration: Develop sophisticated task queuing, scheduling, and distribution systems for parallel processing workflows System Architecture: Design microservices architecture with proper service mesh, inter-service communication, and fault tolerance Performance Optimization: Implement auto-scaling, resource optimization, and system performance monitoring Security & Compliance: Ensure authentication, authorization, data encryption, and security best practices Required Technical Skills: Languages: Python (advanced), Go/Java (preferred for high-performance services) Frameworks: FastAPI/Flask, Django, or equivalent web frameworks Databases: PostgreSQL, Redis, MongoDB (multi-database experience), Milvus Message Queues: RabbitMQ, Apache Kafka Containerization: Docker, Kubernetes (production experience required) Cloud Platforms: OCI/GCP/Azure (at least one, OCI) API Design: RESTful APIs, GraphQL, API versioning and documentation Monitoring: Prometheus, Grafana, ELK stack, distributed tracing Domain-Specific Requirements: Experience with ML pipeline orchestration (Airflow, Prefect, or similar) Understanding of model serving frameworks (MLflow, KubeFlow, Seldon) Experience with high-throughput, low-latency systems Familiarity with observability in ML systems