Technical Project Manager — Data Assets & AI

Advanced Micro Devices, Inc•Austin, TX

1d•Hybrid

About The Position

We are seeking a highly organized and technically savy Project Manager to manage projects across our Data Assets team (data engineers and semantic model developers) and Data Science team (ML and LLM initiatives). This role bridges technical depth with strong program management discipline—ensuring projects are delivered on time, within scope, and aligned to business priorities. You will manage complex data and AI initiatives, coordinate cross-functional teams, and maintain clear communication with stakeholders to drive measurable impact.

Requirements

Experience managing technical projects, preferably in data and AI domains.
Proven track record of delivering complex projects involving data pipelines, semantic models, and ML/AI solutions.
Familiarity with data engineering tools (e.g., Airflow, Snowflake) and semantic modeling (Power BI Tabular, dbt Semantic Layer).
Understanding of machine learning workflows, MLOps practices, and LLM concepts.
Strong command of Agile methodologies and tools (Jira, Azure DevOps).
Excellent communication, stakeholder management, and conflict resolution skills.
Bachelor’s degree in related field.

Responsibilities

Own end-to-end planning and execution for data engineering, semantic modeling, and ML/LLM projects.
Define project scope, timelines, and deliverables; manage risks, dependencies, and change requests.
Run Agile ceremonies, maintain backlogs, and track progress using metrics like velocity and burn-down charts.
Collaborate with data engineers on managing projects related to data pipelines and semantic models.
Partner with data scientists to manage ML/LLM project lifecycles, including experimentation, deployment, and monitoring.
Ensure adherence to best practices in DataOps, MLOps, Release Management and Documentations.
Communicate project status, risks, and decisions clearly to technical and business stakeholders.
Align priorities across teams and manage expectations to avoid delivery surprises.
Implement dashboards and reports for project health and delivery KPIs.
Drive continuous improvement in processes, tools, and team collaboration.