Want to know if this job is worth applying to?
Pune, Maharashtra, India
Onsite
Not Available
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.
