Company: SCRY AI

Location:
Multiple

Experience:
3+ years Employment Type: Full-time

About Us
SCRY AI is an innovative AI-driven technology company focused on building intelligent, scalable, and high-performance solutions. We work with modern technologies across Software Engineering, AI/ML, and Data to solve real-world business problems and deliver impactful digital products. Role Overview We are seeking a skilled Software Engineer – System Scaling with strong hands-on experience in backend engineering, distributed systems, performance optimization, and scalable application architecture. The ideal candidate should be capable of designing, developing, and optimizing systems that handle high traffic, concurrent workloads, and real-time processing, including AI/ML applications deployed across cloud and on-premise environments.

Key Responsibilities
• Design, develop, and maintain scalable, high-performance backend services and distributed applications using Python.
• Design system architectures capable of supporting high concurrency, increasing workloads, and growing user demand.
• Build robust APIs and microservices using frameworks such as FastAPI/Flask, with attention to throughput, latency, and reliability.
• Identify and resolve performance bottlenecks through profiling, load testing, benchmarking, and root-cause analysis.
• Optimize asynchronous processing, concurrency, connection pooling, resource utilization, and background task execution.
• Design and implement horizontal scaling, load balancing, caching, queuing, and distributed processing strategies.
• Work with databases such as PostgreSQL, MySQL, MongoDB, Redis, and vector databases to optimize queries, indexing, connection management,



and data access patterns.
• Implement messaging and event-driven architectures using Kafka, RabbitMQ, or similar technologies.
• Design for fault tolerance, high availability, graceful degradation, retries, timeouts, circuit breakers, and recovery from failures.
• Containerize and deploy applications using Docker and Kubernetes across cloud and on-premise environments.
• Implement system observability through centralized logging, metrics, distributed tracing, alerting, and performance dashboards.
• Optimize AI/ML application serving and inference workloads for latency, throughput, GPU utilization, memory consumption, and cost.
• Establish and execute load, stress, endurance, and scalability tests to validate system performance under different workloads.
• Follow engineering best practices, including Git, CI/CD, automated testing, code reviews, and technical documentation.
• Collaborate with Backend Engineers, Data Scientists, ML Engineers, and Infrastructure teams to deliver reliable, production-ready systems. Key Qualifications
• 3+ years of experience in Software Engineering, Backend Engineering, Distributed Systems, or a related role.
• Robust proficiency in Python, backend development, data structures, algorithms, and software engineering fundamentals.
• Hands-on experience with FastAPI/Flask, REST APIs, microservices,



asynchronous programming, and concurrency.
• Strong understanding of distributed systems, system design, scalability patterns, fault tolerance, and high availability.
• Experience with PostgreSQL/SQL, MongoDB, Redis, or similar databases, including query optimization and connection pooling.
• Familiarity with caching, message queues, event-driven architectures, and distributed processing.
• Experience with Docker, Linux, and Kubernetes, including container orchestration and deployment practices.
• Knowledge of cloud platforms such as AWS, Azure, or GCP and production deployment environments.
• Experience with load testing, profiling, benchmarking, performance tuning, and production debugging.
• Understanding of monitoring, logging, distributed tracing, CI/CD, automated testing, and observability tools.
• Knowledge of networking fundamentals, including TCP/IP, HTTP/HTTPS, DNS, load balancers, and connection management.
• Strong problem-solving, analytical, communication, collaboration, and ownership skills. Good to Have
• Experience scaling GenAI, LLM, RAG, Agentic AI, or real-time Voice AI applications in production.
• Exposure to GPU-based inference optimization, model serving, distributed workloads, and high-throughput AI pipelines.
• Experience with tools such as Prometheus, Grafana, OpenTelemetry, Locust, or k6 for monitoring and performance testing If this role interests you, follow our page to stay updated on more job opportunities and insights into how AI companies build scalable, high-performance systems and reliable infrastructure to support complex enterprise applications.

📌 Software Engineer – System Scaling (Hyderabad)
🏢 Scry AI
📍 Hyderabad