Among your responsibilities, you will:
- Continuously develop Chapter expertise
- 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.
Being a great hands-on engineer and mentoring other team members.
Inspiring and motivating your team.
Comfortable with both planning and execution.
Influencing and being a technical thought partner with Product Owners.
Committing to cross-functional collaboration to achieve the best results for the organization.
Enjoying coaching and developing people to improve their performance, knowledge and expertise.
Are passionate, intellectually curious, and enjoy learning new skills and capabilities.
Bring a data-driven approach to decision-making and problem-solving, both in day-to-day management and in making strategic trade-offs.
Capability to think and work at a systems level, combining data science and engineering skills
BA degree in Computer Science, Statistics, or related field with a focus on Artificial Intelligence, Machine Learning, Natural Language Processing, or similar field preferred
2+ years of experience developing and experimenting with LLMs
6+years of experience developing AI/ML technologies within large and business critical applications
Experience & Skills (Mandatory)
Proficiency in Python and all associated DS libraries and frameworks
Strong knowledge in 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.
Experience & Skills (Nice to have)
Experience with cloud infrastructure for AI/ML
Experience with LLM guardrails
Exeprience with LLM monitoring and observability
Experience with security related to LLM integration
Your AI Engineering chapter, by setting their direction, establishing objectives and key results, working on the staffing and development of your chapter, and ensuring that you maximize outputs and working products.
Tribe Leaders, Product Owners and other Tribe Chapter Leads with whom you shall work to manager chapter resources and ensure a positive collaboration.
Agile Coaches and Scrum Masters, that will ensure that you adopt agile principles, mindset and ways of working into your daily routine and who will coach you during the transformation.
Squad members of a specific squad, led by a Product Owner.