As a Senior Machine Learning Engineer, you should be proficient in building distributed systems, containerization, orchestration, and cloud-native architectures, you will collaborate with cross-functional teams to build scalable, reliable, and production-ready ML systems. Oracle is investing heavily in Generative AI, and as a Senior ML Engineer, you must have a strong understanding and experience in leveraging the capabilities of Large Language Models. 

In this role, you will design, develop, and deploy machine learning models into production systems to solve complex business problems. You will work closely with data scientists, software engineers, and cross-functional teams to build and deploy scalable, production-ready machine learning solutions.

Your expertise will be pivotal in developing Conversational AI solutions and other cutting-edge Machine learning techniques to shape up and enhance users' experience with AI/ML/GenAI capabilities. You will also partner with product managers, software engineers, and operation teams to leverage engineering innovations to simplify the business requirements into scalable solutions.

The ideal candidate is highly technical, particularly around ML and AI, but can lead across the full stack, along with good product sense and business understanding, to map the technology choices to the context of each initiative.

Responsibilities displayed in the job posting

Proficient in creating detailed design specifications and writing elegant code. Lead the incubation of new initiatives, architect scalable solutions, and drive strategic technology choices to develop and deliver AI/ML capabilities for our customers. Develop high-quality, scalable, and efficient software solutions that integrate generative AI capabilities. Design, develop, test, and deploy Machine learning models, including large-language models and build pipelines at scale for batch and real-time use cases. Leverage third-party and in-house ML tools & OCI platform to develop reusable, highly performant machine learning systems with low-latency serving and reliable means to update/re-train ML models. Work collaboratively with cross-functional partners including product managers, operations, and data scientists, identify opportunities for business impact, understand and prioritize requirements for machine learning systems and data pipelines, drive engineering decisions, and quantify impact. Communicate continually with the project teams, and explain progress on the development effort. Ensures quality of work through development standards and QA procedures. Keen on improvising technical solutions and processes emphasizing discovery through doing, iteration, and feedback loops. Attracts, grows, and develops the engineering team with builders and creators. 3+ Experience in developing and supporting ML solutions at scale. 

Career Level - IC3