Key skills
LlamaIndexLangChainVector databasesPineconeNeo4jFAISSMilvusWeaviateLLM APIsOpenAI APIAnthropic APIMistral APIHugging Face APIPythonFastAPIPandasNumPyRetrieval-augmented generation (RAG)EmbeddingsTokenizationPrompt engineeringTool callingChat agent architecturesAIMachine LearningNLPLLMOpenAIClaudeAnthropicLlamaRAGHugging FaceLangGraphVector DatabaseChromaAnalyticsAWSAzureDockerSaaS
About this role
CoreStory is a company focused on unlocking the intelligence in legacy code using AI. They are seeking an AI Engineer to develop and optimize AI components for their narrative intelligence platform, collaborating with various teams to enhance their AI capabilities.
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
Requirements:
- 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
- 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