Description

Key Responsibilities

• Architect and Implement AI Systems: Lead the design and development of end-to-end GenAI solutions, including RAG pipelines and AI agents, from idea to production deployment.
• Knowledge Graph Development: Design, develop, and maintain large-scale knowledge graphs using Neo4j to structure complex, multi-source enterprise data (both structured and unstructured).
• GraphRAG Implementation: Build sophisticated GraphRAG pipelines that integrate vector databases and knowledge graphs to ground AI responses in factual, verifiable information and mitigate hallucinations.
• Model Integration and Optimization: Collaborate with data scientists and ML engineers to prepare data and infrastructure for fine-tuning open-source or proprietary Large Language Models (LLMs) and optimizing them for performance and efficiency.
• Data Pipeline Development: Set up scalable data pipelines for data ingestion, embedding generation, preprocessing, and continuous model training/retraining.
• Technical Leadership & Collaboration: Partner with cross-functional teams (e.g., data engineers, product managers, business stakeholders) to translate complex business needs into robust, scalable AI architectures and provide technical guidance to junior developers.
• Innovation & Best Practices: Stay current with the latest advancements in GenAI, graph databases, and MLOps, advocating for and implementing best practices in CI/CD, testing, and responsible AI. 

Required Skills & Qualifications

• Experience: 7+ years of experience in software development or AI engineering, with a strong portfolio of production-ready AI projects.
• Programming Proficiency: Expert-level proficiency in Python and related AI/ML frameworks (e.g., PyTorch, TensorFlow, LangChain, LlamaIndex).
• Graph Database Expertise: Strong hands-on experience with graph databases, especially Neo4j, including data modeling, Cypher query language, and graph algorithms.
• GenAI & RAG Knowledge: Deep understanding and practical experience with GenAI concepts, LLMs, prompt engineering, embeddings, and building RAG systems.
• Cloud & Infrastructure: Experience in deploying and optimizing models in cloud environments (GCP) and managing project infrastructure.
• Problem-Solving: Excellent analytical and problem-solving skills, with the ability to tackle complex, novel challenges in AI development.
• Education: Bachelor's or master’s degree in computer science, Data Science, Engineering, or a related technical field

Key Skills

Education

Bachelor's or Master's degrees