Key skills
AWSAzureCloudDockerNeo4jNumpyPandasPythonAIMachine LearningNLPLLMOpenAINumPyHugging FaceLangChainVector DatabaseChromaPineconeWeaviateMilvusAnalyticsFastAPI
About this role
Role Overview
- 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).
Tech Stack
- AWS
- Azure
- Cloud
- Docker
- Neo4j
- Numpy
- Pandas
- Python
Benefits
- 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.