Maintaining best-in-class Chapter expertise and resources in order to deliver the right knowledge and skills to Squads.
Staying abreast of relevant developments and innovations in domain area, both internally and externally.
Maintaining your own technical knowledge through learning and continuous improvement.
Develop Chapter members and the Chapter as a whole
Overseeing, providing feedback, and developing Chapter members to help improve domain knowledge and better serve the Squads in which they work.
Discussing Chapter development progress and opportunities with the Tech Area Lead.
Share knowledge and expertise within and outside of the Chapter
Ensuring the “how” of the work performed by Chapter members is aligned with established technical roadmaps and guardrails to drive strategy.
Sharing relevant insights and developments within area of expertise with Chapter members and related Chapters.
Actively sharing knowledge and expertise across the organization with other Chapters.
Identify resource needs throughout the organization
Engaging Product Owners, Tribe Leads, and Tech Area Leads to allocate Chapter members and ensure Squads have proper technical resourcing and functional expertise.
Identifying expertise and resource gaps and training or hiring the talent needed to address them.
Work in a Squad to realize its mission
Advancing the work of the Squad based on items in the backlog and priorities set by the Product Owner.
Sharing expertise with Squad members and working cross-functionally to advance the work of the Squad.
Enable the organization’s new way of working
Modelling behaviors to support the organization’s transformation to a new way of working.
Actively creating and maintaining a positive culture within the Chapter based on Agile leadership behaviors.
Requirements & Skills:
Master’s degree in Computer Science, Statistics, or related field with a focus on Artificial Intelligence, Machine Learning, or Natural Language Processing[
2+ years of experience developing and experimenting with LLMs
6+years of experience developing AI/ML technologies within large and business-critical applications
Proficiency in Python and all associated DS libraries and frameworks
Strong knowledge of AI, machine learning, and natural language processing
Experience with leveraging, training, and fine-tuning Foundation Models, including multimodal inputs and outputs
Strong experience working with key LLM models APIs (e.g. OpenAI, Anthropic) and LLM Frameworks (e.g. LangChain, LlamaIndex)
Experience with multi-agent frameworks/systems and an understanding of multi-agent systems and their applications in complex problem-solving scenarios.
Experience with unstructured.io or similar libraries for handling various document formats and extracting structured information from unstructured data.
Expertise in using LlamaIndex for building and querying knowledge bases, including its data connectors, indexing strategies, and query engines.
Knowledge of effective text chunking techniques for optimal processing and indexing of large documents or datasets.
Proficiency in generating and working with text embeddings using models like BERT, GPT, or domain-specific embedding models. Understanding of embedding spaces and their applications in semantic search and information retrieval.
Experience in constructing and querying knowledge graphs, including technologies like Neo4j or RDF triplestores. Understanding of ontology design and graph-based reasoning.
Experience with RAG concepts and fundamentals (vectorDBs, semantic search, etc.), Expertise in implementing RAG systems that combine knowledge bases with generative AI models.
