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.