Job Description
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, 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.
Qualifications and experience
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, relevant professional experience as 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 finetuning 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 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.
Responsibilities
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, relevant professional experience as 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 finetuning 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.
Disclaimer:
Certain US customer or client-facing roles may be required to comply with applicable requirements, such as immunization and occupational health mandates.
Range and benefit information provided in this posting are specific to the stated locations only
US: Hiring Range in USD from: $141,800 to $232,200 per annum. May be eligible for equity and compensation deferral. Eligible for commission with an estimated pay mix of 70/30.
Oracle maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, market conditions and locations, as well as reflect Oracle's differing products, industries and lines of business.
Candidates are typically placed into the range based on the preceding factors as well as internal peer equity.
Oracle US offers a comprehensive benefits package which includes the following:
Medical, dental, and vision insurance, including expert medical opinion
Short term disability and long term disability
Life insurance and AD&D
Supplemental life insurance (Employee/Spouse/Child)
Health care and dependent care Flexible Spending Accounts
Pre-tax commuter and parking benefits
401(k) Savings and Investment Plan with company match
Paid time off: Flexible Vacation is provided to all eligible employees assigned to a salaried (non-overtime eligible) position. Accrued Vacation is provided to all other employees eligible for vacation benefits. For employees working at least 35 hours per week, the vacation accrual rate is 13 days annually for the first three years of employment and 18 days annually for subsequent years of employment. Vacation accrual is prorated for employees working between 20 and 34 hours per week. Employees working fewer than 20 hours per week are not eligible for vacation.
11 paid holidays
Paid sick leave: 72 hours of paid sick leave upon date of hire. Refreshes each calendar year. Unused balance will carry over each year up to a maximum cap of 112 hours.
Paid parental leave
Adoption assistance
Employee Stock Purchase Plan
Financial planning and group legal
Voluntary benefits including auto, homeowner and pet insurance
The role will generally accept applications for at least three calendar days from the posting date
