About CoreStory CoreStory unlocks the hidden intelligence in your legacy code. By using AI to surface business logic and technical insights we give enterprises the clarity to modernize faster maintain apps smarter and reduce the risk of costly failures. We’re looking for an AI Engineer who is passionate about building intelligent systems that blend large language models retrieval architectures and conversational agents into cohesive scalable products. This role is critical to the core AI engine powering the CoreStory Platform. Role Overview As an AI Engineer you’ll play a central role in developing and optimizing the AI components that power CoreStory’s narrative intelligence platform. You’ll work across LLM integration vector search systems prompt orchestration agentic systems and retrieval-augmented generation (RAG) pipelines. You’ll collaborate closely with the product data and infrastructure teams to prototype productionize and continuously evolve our AI stack — ensuring that our systems are accurate explainable efficient and on the cutting edge of modern AI capabilities. Key Responsibilities Design implement and optimize LLM-powered systems (e.g. RAG chat agents summarizers knowledge graph integration). Build and manage data indexing and retrieval pipelines using LlamaIndex LangChain or similar frameworks. Implement and maintain vector databases (e.g. Pinecone Neo4j Weaviate Chroma or Azure Cognitive Search). Integrate open-source and proprietary LLMs (e.g. GPT Claude Llama) into the CoreStory Platform. Develop and refine AI-driven features — including generative insights automated summarization and narrative analytics. Collaborate with DevOps and backend teams to deploy scalable AI services within CoreStory’s cloud infrastructure. Continuously benchmark model performance latency and cost identifying opportunities for optimization. Stay current with advancements in AI — from model architectures to emerging frameworks — and propose innovative applications aligned with CoreStory’s mission. Contribute to internal documentation experimentation frameworks and evaluation methodologies. Qualifications Required Skills 7+ years of overall engineering experience with at least 3+ years of experience in AI engineering machine learning or applied NLP. Strong hands-on experience with LlamaIndex LangChain or similar orchestration frameworks. Experience designing and implementing vector database solutions (e.g. Pinecone Neo4j FAISS Milvus Weaviate Solid understanding of LLM APIs (OpenAI Anthropic Mistral Hugging Face etc.). Proficiency in Python with experience in libraries such as FastAPI Pandas or NumPy Understanding of retrieval-augmented generation (RAG) patterns embeddings and tokenization. Familiarity with prompt engineering tool calling and chat agent architectures Strong problem-solving and analytical mindset with attention to performance and scalability. Demonstrated interest in staying up-to-date with the fast-evolving AI landscape. Preferred Experience deploying AI services in production (e.g. using Docker Azure or AWS). Exposure to LangGraph semantic search or hybrid RAG systems. Familiarity with knowledge graphs document intelligence or multimodal AI Previous experience in SaaS or early-stage startup environments. What We Offer Competitive compensation and equity. Flexible remote-first work environment. Opportunities to define and build the AI roadmap of a fast-growing technology company. Collaborative learning-oriented culture. Access to cutting-edge AI models research and infrastructure.