{"id":4311829,"url":"https://alion.io/job/showpad-principal-software-engineer","title":"Principal Software Engineer","company":{"id":54384,"name":"Showpad","domain":"showpad.com","url":"https://alion.io/company/showpad","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":95,"open_postings":4,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":74,"computed_at":"2026-11-14T05:58:10Z"}},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon Neptune","optional":false},{"name":"Anthropic","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Docker","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Go","optional":false},{"name":"GraphRAG","optional":false},{"name":"Incident Management","optional":false},{"name":"Java","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Llama","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Milvus","optional":false},{"name":"Mistral","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Neo4j","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Redis","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Search","optional":false},{"name":"Terraform","optional":false},{"name":"Weaviate","optional":false}],"status":"live","first_seen_at":"2026-11-14T05:07:58Z","employer_posted_date":"2026-11-14","last_verified_at":"2026-11-14T12:30:28Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Job Description: Principal Software Engineer - AI Platforms & Knowledge Graphs\nRole: Principal Software Engineer - AI/LLM Platforms\nExperience: 10-15+ years\nLevel: Principal Engineer / Technical Architect\nDomain: Enterprise AI, Knowledge Graphs, Generative AI, Distributed Systems\nAbout the Role\nWe 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.\nThe ideal candidate brings deep expertise in distributed systems, scalable software architecture, knowledge graphs, Large Language Models (LLMs), and production-grade AI engineering.\nThis 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.\nThe 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.\nKey Responsibilities\n1. Enterprise Knowledge Graph Engineering\n- Architect, design, develop, and maintain scalable enterprise knowledge graph platforms.\n- Build knowledge ingestion, entity extraction, relationship mapping, entity resolution, and graph enrichment pipelines.\n- Design graph data models, ontologies, taxonomies, and semantic relationships.\n- Develop high-performance graph querying, retrieval, and reasoning capabilities.\n- Integrate knowledge graphs with enterprise data sources, applications, and AI systems.\n- Implement GraphRAG, hybrid retrieval, and context-aware search architectures.\n- Ensure scalability, reliability, data consistency, and performance of graph-based systems.\n2. AI & LLM Platform Engineering\n- Lead the architecture and development of production-grade AI applications leveraging Large Language Models.\n- Design and implement RAG, GraphRAG, agentic AI, and intelligent workflow solutions.\n- Integrate and orchestrate multiple proprietary and open-source LLMs, including GPT, Claude, Gemini, Llama, and other emerging models.\n- Evaluate LLM capabilities, reasoning quality, latency, context handling, accuracy, cost, and suitability for different use cases.\n- Develop model-agnostic architectures enabling flexible model selection, routing, and fallback strategies.\n- Optimize prompt engineering, context management, inference performance, token consumption, and operational costs.\n- Build LLM evaluation frameworks, monitoring, guardrails, and observability capabilities.\n- Address hallucinations, retrieval quality, grounding, security, and reliability challenges in enterprise AI applications.\n3. Scalable Systems Architecture\n- Architect highly scalable, fault-tolerant, distributed systems supporting enterprise workloads.\n- Design cloud-native, microservices-based, asynchronous, and event-driven architectures.\n- Build high-throughput data pipelines and low-latency APIs.\n- Drive performance engineering, horizontal scaling, caching, concurrency, and infrastructure optimization.\n- Design secure, multi-tenant systems with robust isolation and enterprise-grade availability.\n- Make architectural trade-offs across performance, reliability, maintainability, and cost.\n4. Production Engineering & Platform Reliability\n- Own the end-to-end engineering lifecycle, from architecture and implementation to production deployment and ongoing maintenance.\n- Establish engineering standards for CI/CD, automated testing, deployment, monitoring, and incident management.\n- Implement observability, distributed tracing, performance monitoring, and operational diagnostics.\n- Drive system reliability, availability, security, and operational excellence.\n- Troubleshoot complex production issues across distributed systems, graph databases, and LLM integrations.\n- Continuously improve scalability, response times, resource utilization, and infrastructure costs.\n5. Technical Leadership\n- Provide technical direction and architectural leadership across engineering teams.\n- Lead complex system design discussions, architecture reviews, and technology evaluations.\n- Mentor senior engineers and establish strong software engineering practices.\n- Collaborate with product, AI/ML, data engineering, infrastructure, and customer-facing teams.\n- Translate complex enterprise requirements into robust technical solutions.\n- Take ownership of critical architectural decisions and long-term platform evolution.\n- Remain hands-on with design, coding, prototyping, debugging, and performance optimization.\nRequired Qualifications & Experience\n- 10-15+ years of software engineering experience, including significant experience designing and operating large-scale distributed systems.\n- Strong proficiency in Python, Java, Go, or equivalent backend programming languages.\n- Demonstrated experience architecting, developing, and maintaining enterprise-scale software platforms.\n- Hands-on experience building and deploying knowledge graph or graph-based data systems.\n- Strong understanding of graph databases, graph algorithms, semantic modeling, and knowledge retrieval.\n- Practical experience building production AI applications using LLMs.\n- Experience integrating multiple LLM providers and understanding model-specific capabilities, limitations, and engineering trade-offs.\n- Strong understanding of RAG architectures, embeddings, vector databases, and hybrid retrieval.\n- Expertise in cloud technologies such as AWS, Azure, or GCP.\n- Experience with microservices, containers, Kubernetes, APIs, and distributed data processing.\n- Strong knowledge of system design, scalability, concurrency, reliability, and performance optimization.\n- Proven ability to independently drive complex engineering initiatives from design to production.\nPreferred Technical Experience\nKnowledge Graph & Data Technologies\n- Neo4j, Amazon Neptune, RDF, SPARQL, Cypher\n- Ontology Modeling, Entity Resolution, Graph Algorithms\n- Elasticsearch, OpenSearch, Graph Databases\n- Vector Databases: Pinecone, Weaviate, Milvus, pgvector\nGenerative AI & LLM Frameworks\n- OpenAI, Anthropic Claude, Google Gemini, Llama, Mistral\n- LangChain, LlamaIndex, LangGraph\n- GraphRAG, Agentic RAG, Hybrid RAG\n- Embedding Models, Reranking, Semantic Search\n- Model Context Protocol (MCP), Tool Calling\n- LLM Evaluation, Observability, Guardrails\nScalable Architecture & Infrastructure\n- AWS / Azure / GCP\n- Docker, Kubernetes, Terraform\n- Kafka, Redis, PostgreSQL\n- Distributed Systems, Microservices, Event-Driven Architecture\n- CI/CD, Monitoring, Distributed Tracing\n- High Availability, Fault Tolerance, Performance Engineering\nWhat Makes an Ideal Candidate\nWe are particularly interested in engineers who:\n- Have built and operated real enterprise-scale systems, not just prototypes or proof-of-concepts.\n- Have designed knowledge graph platforms and solved real-world challenges around ingestion, retrieval, graph performance, and scalability.\n- Understand the nuances of different LLMs and know when and why to use a particular model.\n- Have experience taking AI applications from experimentation to secure, reliable, cost-efficient production deployments.\n- Can operate comfortably across AI engineering, backend systems, data architecture, and cloud infrastructure.\n- Are strong hands-on engineers who can influence architecture and engineering practices across multiple teams.\n- Demonstrate deep technical judgment, ownership, and the ability to solve ambiguous, complex engineering problems.\nSuccess in This Role\nSuccess will be measured by the ability to:\n- Deliver robust and scalable knowledge graph and AI platform capabilities.\n- Improve platform performance, reliability, and operational efficiency.\n- Accelerate the development and production deployment of enterprise AI applications.\n- Establish reusable architectural patterns and engineering standards.\n- Reduce technical complexity and improve long-term platform maintainability.\n- Enable engineering teams to deliver complex AI solutions with greater speed and quality.\n---\nCore Keywords: Principal Software Engineer, AI Platform Architect, Enterprise Knowledge Graph, GraphRAG, Generative AI, LLM Engineering, Multi-LLM Orchestration, Agentic AI, RAG, Distributed Systems, Scalable Architecture, Graph Databases, Cloud-Native Systems, LLMOps, AI Observability, Enterprise AI Applications, Technical Leadership.","description_format":"text","description_chars":8543,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Sales Enablement","Corporate 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