Company Description

We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale — across all devices and digital mediums, and our people exist everywhere in the world (19000+ experts across 33 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!

Job Description

• 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 Llama Index 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.
• 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 Llama Index 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.

Qualifications

Must have Skills: Python for Data Science (Capable).\

Good To Have Skills: Machine Learning on AWS (Capable), Generative AI Fundamentals (Capable).