Principal Architect AI Computational Scientist

Principal Architect AI Computational Scientist at Oracle

Oracle Cloud Infrastructure (OCI) is a pioneering force in cloud technology, merging the agility of startups with the robustness of an enterprise software leader. Within OCI, the Oracle AI Infra / Gen AI Cloud Engineering team spearheads innovative solutions at the convergence of artificial intelligence and cloud infrastructure. As part of this team, you'll contribute to large-scale cloud solutions utilizing cutting-edge machine learning technologies, aimed at addressing complex global challenges.

Join us to create innovative solutions using top-notch machine learning technologies to solve global challenges. We're looking for an experienced Principal Applied Data/Computational Scientist to join our Cloud Engineering team for strategic customers. In this role, you'll collaborate with applied scientists and product managers to design, develop, and deploy tailored Gen-AI solutions with an emphasis on Large Language Models (LLMs), Agents, Model Context Protocol (MCP), and Retrieval Augmented Generation (RAG) with large OpenSearch clusters. You will be responsible for identifying, solutioning, and implementing AI solutions to the corresponding GPU IaaS or PaaS.

Key Responsibilities and Qualifications

• Doctoral or master’s degree in computer science or equivalent technical field with 10+ years of experience.
• Able to optimally communicate technical ideas verbally and in writing (technical proposals, design specs, architecture diagrams and presentations).
• Demonstrated experience in designing and implementing scalable AI models and solutions for production, with relevant professional experience as an end-to-end solutions engineer or architect (data engineering, data science, and ML engineering is a plus), with evidence of close collaborations with PM and Dev teams.
• Experience with OpenSearch, Vector databases, PostgreSQL, and Kafka Streaming.
• Practical experience with setting up and fine-tuning large OpenSearch Clusters.
• Experience in setting up data ingestion pipelines with OpenSearch.
• Experience with search algorithms, indexing, optimizing latency, and response times.
• Practical experience with the latest technologies in LLM and generative AI, such as parameter-efficient fine-tuning, instruction fine-tuning, and advanced prompt engineering techniques like Tree-of-Thoughts.
• Familiarity with Agents and Agent frameworks and Model Context Protocol (MCP).
• Hands-on experience with emerging LLM frameworks and plugins, such as LangChain, LlamaIndex, VectorStores and Retrievers, LLM Cache, LLMOps (MLFlow), LMQL, Guidance, etc.
• Strong publication record, including as a lead author or reviewer, in top-tier journals or conferences.
• Ability and passion to mentor and develop junior machine learning engineers.
• Proficient in Python and shell scripting tools.

Preferred Qualifications

• PhD/Masters in a related field with 5+ years relevant experience.
• Experience with RAG-based solutions architecture.
• Familiarity in OpenSearch and Vector stores as a knowledge store.
• Knowledge of LLM and experience delivering Generative AI and Agent models are a significant plus.
• Familiarity and experience with the latest advancements in computer vision and multimodal modeling is a plus.
• Experience with semantic search, multi-modal search, and conversational search.
• Experience in working on a public cloud environment, and in-depth knowledge of IaaS/PaaS industry and competitive capabilities.
• Experience with popular model training and serving frameworks like KServe, KubeFlow, Triton, etc.
• Experience with LLM fine-tuning, especially the latest parameter-efficient fine-tuning technologies and multi-task serving technologies.
• Deep technical understanding of Machine Learning, Deep Learning architectures like Transformers, training methods, and optimizers.
• Experience with deep learning frameworks (such as PyTorch, JAX, or TensorFlow) and deep learning architectures (especially Transformers).
• Experience in diagnosing, fixing, and resolving issues in AI model training and serving.

Key skills/competency

• AI Solution Design
• Generative AI
• Large Language Models (LLM)
• Retrieval Augmented Generation (RAG)
• Cloud Architecture
• Machine Learning
• Data Engineering
• OpenSearch
• Vector Databases
• Python