Machine Learning Architect, Salesforce

• 15+ years in Machine Learning & Search Systems
• Extensive experience with large-scale search, ML, and knowledge-driven systems, specifically focused on integrating Knowledge Graphs, search optimization, and advanced retrieval techniques
• Extensive background in RAG platform
• Deep expertise in Retrieval-Augmented Generation (RAG) platforms, including vector databases, retriever-reranker architectures, and integration with LLMs for scalable, accurate information retrieval
• Expertise in Semantic and Vector-Based Search
• Deep knowledge of vector databases (e.g., FAISS, Pinecone, Milvus), approximate nearest neighbor (ANN) search algorithms, and embedding techniques to power high-relevance search systems
• Strong Background in NLP & LLMs
• Experience with natural language processing (NLP), prompt engineering, and utilizing LLMs to enhance knowledge-based search and retrieval in enterprise contexts
• Advanced Knowledge Graph Skills
• Proficiency in graph databases (e.g., Neo4j, Amazon Neptune), graph embedding, and linking techniques to enable rich contextual search and high-dimensional graph-based retrieval
• Proficiency in Distributed Systems & ML Frameworks
• Advanced understanding of distributed systems, data streaming (e.g., Kafka, Spark), and ML frameworks (TensorFlow, PyTorch) to support real-time, resilient AI applications
• Programming Mastery in Python & Graph-Based Frameworks
• Strong programming skills in Python, with expertise in machine learning and graph-based frameworks to facilitate scalable, high-performance AI solutions
• If you’re an industry leader passionate about RAG, Search, Knowledge Graphs, and cutting-edge AI, and eager to make an impact at the world’s #1 CRM company, we’d love to meet you!

Desirable

• Our Einstein Foundation team is an interdisciplinary mix of machine learning engineers, data scientists, and software engineers working collaboratively to build adaptive, context-aware systems that elevate customer interactions and insights
• Our team culture values innovation, cross-functional collaboration, and a commitment to scaling AI-driven customer success solutions
• In this role, you will architect and drive the development of RAG and Search solutions at scale, integrating the latest advancements in machine learning, LLMs, and vector databases
• You’ll be responsible for leading the end-to-end AI lifecycle, from ideation through production, focusing on scalable search and retrieval architectures optimized for enterprise use cases
• As a thought leader, you will define best practices and collaborate closely with Product Managers, Data Scientists, and Research teams to shape and deliver groundbreaking AI experiences
• Architect and implement end-to-end, large-scale search and retrieval solutions that leverage Knowledge Graphs and are optimized for high-performance, multi-tenant environments
• Innovate hybrid retrieval pipelines combining semantic, vector, and symbolic search to improve contextual relevance, speed, and accuracy in knowledge-driven AI applications
• Enhance system efficiency through automation in capacity planning, configuration, and proactive monitoring, driving real-time search optimization
• Work closely with cross-functional teams, including Product Managers, Knowledge Engineers, and ML Researchers, to capture requirements and translate them into scalable, cutting-edge search and retrieval solutions
• Guide discussions on emerging technologies and advancements in vector search, graph embeddings, and knowledge-augmented retrieval, fostering a culture of continuous innovation