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.
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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.