Principal Software Engineer

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. 5. 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. --- 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.