Senior Asset Owner

Responsibilities

• Define and maintain a roadmap for enhancing AI and ML ecosystem to support a rapidly expanding set of use cases, capabilities, and environments.
• Build Strategy for Agentic AI & ML and become an influencer in promoting best practices to the enterprise AI practitioners.
• Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques to ensure ongoing competitive advantage.
• Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations.
• Accountable for ownership of design, development and maintenance of Model as Service.
• Collaborate with partners Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams.
• Collaborate with partner teams to create and maintain Practitioners Guide for AI Platform adoption.
• Own the delivery of critical milestones for model deployment in the AWS cloud.
• Adept at adopting AI Platform best practices within the team and promoting to the Data Science and AI practitioners community.
• Participate in Agile ceremonies, define User Stories and collaborate with Scrum Master to execute sprint.
• Provide detailed product status and summary reports to leaders in a regular cadence.

Requirements

• Must be authorized to work in the U.S. now and in the future.
• Master’s degree in related field or 5+ years of equivalent experience in a research function.
• Experience leading and managing software development teams.
• Good understanding of DevSecOps, MLOps and AgentOps LifeCycle and Agile methodology.
• Experience in end to end model development lifecycle, Gen AI and Agents development from ideation through post production.
• Delivery of critical milestones for model deployment in the cloud will be the primary success criteria for this position. Understanding the Data Science Model Development Process (CRISP-DM) and the IT Software Development Lifecycle (SDLC) is essential for this role.
• Familiar with Solution Design and Architecture of data and ML pipelines.

Preferred Skills

• Familiarity with GCP, Vertex AI, AWS, Sage Maker, Python Flask.
• Experience working with Docker, Kubernetes and EC2 environment.
• Basic understanding of ML frameworks i.e. Tensorflow, Anacoda, Scikit Learn, H20.
• Demonstrable interest in data science or predictive analytics.
• Ability to work well with a distributed team.
• Experience with Scrum and Agile framework.