Principal Software Engineer

Showpad
Pune, India

Who this role is best for

Best suited to technically accomplished engineers with a background in AI and distributed systems working in enterprise environments.

Best fit for

  • Candidates with 10–15+ years of experience in AI and distributed systems architecture
    — “10–15+ years of software engineering experience, including significant experience designing and operating large-scale distributed systems.”
  • Professionals with a track record of building and deploying knowledge graph systems
    — “Hands-on experience building and deploying knowledge graph or graph-based data systems.”

Things to consider

  • The role requires ownership of complex engineering initiatives from design to production
    — “Proven ability to independently drive complex engineering initiatives from design to production.”
  • Candidates should be prepared to make trade-offs across performance, reliability, and cost
    — “Make architectural trade-offs across performance, reliability, maintainability, and cost.”

How to stand out

  • Highlight your experience in building and optimizing enterprise knowledge graph pipelines
    — “Build knowledge ingestion, entity extraction, relationship mapping, entity resolution, and graph enrichment pipelines.”
  • Emphasize your leadership in AI/LLM platform architecture and integration
    — “Lead the architecture and development of production-grade AI applications leveraging Large Language Models.”
  • Demonstrate your ability to evaluate and integrate multiple LLM providers
    — “Experience integrating multiple LLM providers and understanding model-specific capabilities, limitations, and engineering trade-offs.”
  • Showcase your skills in designing cloud-native, scalable distributed systems
    — “Architect highly scalable, fault-tolerant, distributed systems supporting enterprise workloads.”
  • Include examples of your work in system reliability, observability, and performance optimization
    — “Drive system reliability, availability, security, and operational excellence.”
Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • architect and develop scalable enterprise knowledge graph platforms
  • design and implement RAG and GraphRAG solutions
  • build high-throughput data pipelines
  • optimize prompt engineering and inference performance
Typical background
distributed systemsknowledge graphsAI engineering

Skills & requirements

Required

Distributed SystemsKnowledge GraphsLarge Language ModelsAI EngineeringEnterprise-scale AI PlatformsLLM IntegrationCloud-native ArchitectureMicroservicesPerformance EngineeringProduction Engineering

Preferred

Graph Data ModelsOntologiesTaxonomiesSemantic RelationshipsLLM EvaluationPrompt EngineeringContext ManagementSecurityReliability

About the role

Original posting from Showpad via Greenhouse

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

  • 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.
  • 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.
  • 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.
  • 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.
  • Technical Leadership
  • Provide technical direction and architectural leadership across engineering teams.
  • Lead complex system design discussions, architecture reviews, and technology evaluations.
  • Mentor senior engineers and establish strong software engineering practices.
  • Collaborate with product, AI/ML, data engineering, infrastructure, and customer-facing teams.
  • Translate complex enterprise requirements into robust technical solutions.
  • Take ownership of critical architectural decisions and long-term platform evolution.
  • Remain hands-on with design, coding, prototyping, debugging, and performance optimization.

Required Qualifications & Experience

  • 10–15+ years of software engineering experience, including significant experience designing and operating large-scale distributed systems.
  • Strong proficiency in Python, Java, Go, or equivalent backend programming languages.
  • Demonstrated experience architecting, developing, and maintaining enterprise-scale software platforms.
  • Hands-on experience building and deploying knowledge graph or graph-based data systems.
  • Strong understanding of graph databases, graph algorithms, semantic modeling, and knowledge retrieval.
  • Practical experience building production AI applications using LLMs.
  • Experience integrating multiple LLM providers and understanding model-specific capabilities, limitations, and engineering trade-offs.
  • Strong understanding of RAG architectures, embeddings, vector databases, and hybrid retrieval.
  • Expertise in cloud technologies such as AWS, Azure, or GCP.
  • Experience with microservices, containers, Kubernetes, APIs, and distributed data processing.
  • Strong knowledge of system design, scalability, concurrency, reliability, and performance optimization.
  • Proven ability to independently drive complex engineering initiatives from design to production.

Preferred Technical Experience

Knowledge Graph & Data Technologies

  • Neo4j, Amazon Neptune, RDF, SPARQL, Cypher
  • Ontology Modeling, Entity Resolution, Graph Algorithms
  • Elasticsearch, OpenSearch, Graph Databases
  • Vector Databases: Pinecone, Weaviate, Milvus, pgvector

Generative AI & LLM Frameworks

  • OpenAI, Anthropic Claude, Google Gemini, Llama, Mistral
  • LangChain, LlamaIndex, LangGraph
  • GraphRAG, Agentic RAG, Hybrid RAG
  • Embedding Models, Reranking, Semantic Search
  • Model Context Protocol (MCP), Tool Calling
  • LLM Evaluation, Observability, Guardrails

Scalable Architecture & Infrastructure

  • AWS / Azure / GCP
  • Docker, Kubernetes, Terraform
  • Kafka, Redis, PostgreSQL
  • Distributed Systems, Microservices, Event-Driven Architecture
  • CI/CD, Monitoring, Distributed Tracing
  • High Availability, Fault Tolerance, Performance Engineering

What Makes an Ideal Candidate

We are particularly interested in engineers who:

  • Have built and operated real enterprise-scale systems, not just prototypes or proof-of-concepts.
  • Have designed knowledge graph platforms and solved real-world challenges around ingestion, retrieval, graph performance, and scalability.
  • Understand the nuances of different LLMs and know when and why to use a particular model.
  • Have experience taking AI applications from experimentation to secure, reliable, cost-efficient production deployments.
  • Can operate comfortably across AI engineering, backend systems, data architecture, and cloud infrastructure.
  • Are strong hands-on engineers who can influence architecture and engineering practices across multiple teams.
  • Demonstrate deep technical judgment, ownership, and the ability to solve ambiguous, complex engineering problems.

Success in This Role

Success will be measured by the ability to:

  • Deliver robust and scalable knowledge graph and AI platform capabilities.
  • Improve platform performance, reliability, and operational efficiency.
  • Accelerate the development and production deployment of enterprise AI applications.
  • Establish reusable architectural patterns and engineering standards.
  • Reduce technical complexity and improve long-term platform maintainability.
  • Enable engineering teams to deliver complex AI solutions with greater speed and quality.

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Core 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.

Source: Showpad careers (Greenhouse)

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