# Principal Software Engineer at Showpad > India · Principal · Software engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Ashowpad%3A8387598 (read 2026-11-14). ## Facts (observed) - Company: [Showpad](https://alderhq.app/companies/showpad) - Location text: Pune - Where it can be done from (inferred): India (evidence: "Location lists "pune"") - Posted: 2026-11-14 (1 days ago) - First seen by alder: 2026-11-14 · last verified: 2026-11-14 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://job-boards.greenhouse.io/showpad/jobs/8387598 - Skills listed (inferred): LLMs, Distributed systems ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (Pune, n 5): p25 ₹25.2L, median ₹32.3L, p75 ₹39L - Licensed UK sponsor: registered as "Showpad Ltd" (checked 2026-11-09, [register](https://www.gov.uk/government/publications/register-of-licensed-sponsors-workers)) ## Description (the employer's text) Job Description: Principal Software Engineer – AI Platforms & Knowledge Graphs Role: Principal Software Engineer – AI/LLM Platforms Experience: 10–15+ years Level: Principal Engineer / Technical Architect Domain: Enterprise AI, Knowledge Graphs, Generative AI, Distributed Systems About the Role We are looking for a highly accomplished Principal Software Engineer to lead the architecture, design, development, and evolution of enterprise-scale AI platforms, knowledge graph systems, and intelligent applications. The ideal candidate brings deep expertise in distributed systems, scalable software architecture, knowledge graphs, Large Language Models (LLMs), and production-grade AI engineering. This is a hands-on technical leadership role requiring strong engineering fundamentals, architectural depth, and the ability to build, deploy, scale, and maintain complex AI-powered systems for enterprise customers. The candidate should have practical experience working with multiple LLMs, understand their capabilities, limitations, and performance trade-offs, and be capable of making informed architectural decisions to deliver reliable, secure, and cost-efficient AI solutions. Key Responsibilities 1. Enterprise Knowledge Graph Engineering - Architect, design, develop, and maintain scalable enterprise knowledge graph platforms. - Build knowledge ingestion, entity extraction, relationship mapping, entity resolution, and graph enrichment pipelines. - Design graph data models, ontologies, taxonomies, and semantic relationships. - Develop high-performance graph querying, retrieval, and reasoning capabilities. - Integrate knowledge graphs with enterprise data sources, applications, and AI systems. - Implement GraphRAG, hybrid retrieval, and context-aware search architectures. - Ensure scalability, reliability, data consistency, and performance of graph-based systems. 2. AI & LLM Platform Engineering - Lead the architecture and development of production-grade AI applications leveraging Large Language Models. - Design and implement RAG, GraphRAG, agentic AI, and intelligent workflow solutions. - Integrate and orchestrate multiple proprietary and open-source LLMs, including GPT, Claude, Gemini, Llama, and other emerging models. - Evaluate LLM capabilities, reasoning quality, latency, context handling, accuracy, cost, and suitability for different use cases. - Develop model-agnostic architectures enabling flexible model selection, routing, and fallback strategies. - Optimize prompt engineering, context management, inference performance, token consumption, and operational costs. - Build LLM evaluation frameworks, monitoring, guardrails, and observability capabilities. - Address hallucinations, retrieval quality, grounding, security, and reliability challenges in enterprise AI applications. 3. Scalable Systems Architecture - Architect highly scalable, fault-tolerant, distributed systems supporting enterprise workloads. - Design cloud-native, microservices-based, asynchronous, and event-driven architectures. - Build high-throughput data pipelines and low-latency APIs. - Drive performance engineering, horizontal scaling, caching, concurrency, and infrastructure optimization. - Design secure, multi-tenant systems with robust isolation and enterprise-grade availability. - Make architectural trade-offs across performance, reliability, maintainability, and cost. 4. Production Engineering & Platform Reliability - Own the end-to-end engineering lifecycle, from architecture and implementation to production deployment and ongoing maintenance. - Establish engineering standards for CI/CD, automated testing, deployment, monitoring, and incident management. - Implement observability, distributed tracing, performance monitoring, and operational diagnostics. - Drive system reliability, availability, security, and operational excellence. - Troubleshoot complex production issues across distributed systems, graph databases, and LLM integrations. - Continuously improve scalability, response times, resource utilization, and infrastructure costs. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Ashowpad%3A8387598 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.