Software Development Engineer Intern – Backend at Smytten
On-site · Bengaluru, Karnataka, India · intern
Practice the Software Development Engineer Intern – Backend interview at Smytten with an AI interviewer: voice, live coding and a scorecard.
Skills the interview covers
- Data structures
- Algorithms
- Backend development in Python, Java, Go, Node.js or equivalent
- REST/gRPC APIs
- SQL
- Cloud-native application development
- Caching
- Queues
- Asynchronous processing
About the role
Design and build highly scalable, secure and fault-tolerant backend systems powering enterprise-grade AI applications across Consumer Research and Analytics. Build the orchestration layer for AI and agentic workflows — connecting LLMs, analytical models, data sources, research engines and enterprise applications into reliable production systems. Develop high-performance APIs, microservices and asynchronous processing systems that power AI-led research, analytics, dashboards, automated insight generation and enterprise integrations. Build backend infrastructure for GenAI applications, including model gateways, prompt and workflow orchestration, structured outputs, RAG pipelines, embeddings, vector retrieval, caching, evaluation and AI guardrails. Build AI systems that operate across structured and unstructured data — survey responses, behavioural events, transactions, documents, conversations, qualitative interviews and external intelligence sources. Engineer multi-tenant enterprise architecture with strong data isolation, role-based access controls, auditability, authentication, API security and privacy-by-design principles. Build scalable research and analytics workflow engines capable of managing long-running jobs, distributed tasks, retries, queues, scheduling and real-time status updates. Create production-grade integrations with LLMs and machine-learning models, while solving for latency, reliability, model fallbacks, token economics, observability and output quality. Build systems that make AI explainable and auditable by maintaining lineage between source data, analytical transformations, model outputs and final recommendations. Work closely with AI/ML engineers, data scientists, researchers, product teams and analytics experts to convert complex research and analytical methodologies into scalable software products. Continuously experiment with new developments across LLMs, AI agents, retrieval systems, reasoning models and data infrastructure, and translate the ones that matter into production capabilities.