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.

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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.