Senior Applied Data & AI Engineer Austin, TX
Listed on 2026-05-15
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IT/Tech
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Engineering, Data Scientist
WHAT YOU DO AT AMD CHANGES EVERYTHING
We care deeply about transforming lives with AMD technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next‑generation computing experiences - the building blocks for the data center, artificial intelligence, PCs, gaming and embedded. Underpinning our mission is the AMD culture. We push the limits of innovation to solve the world's most important challenges.
We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives.
AMD together we advance_
THE ROLE:We are seeking a seasoned Senior Applied Data & AI Engineer to design and execute impactful data and AI solutions. This role is ideal for professionals with expertise spanning data engineering, data science, and machine learning, capable of building scalable data systems, developing AI models, and integrating solutions into business workflows. As a full‑stack applied data & AI engineer, you will wear multiple hats, working across the entire data lifecycle, from pipeline creation and data preparation to AI model deployment and optimization, ensuring seamless delivery of impactful solutions.
This position offers the opportunity to work on complex projects, applying advanced technical expertise to solve challenging problems and deliver measurable results.
This AMD (Advanced Micro Devices) team is looking for a passionate senior‑level person who can help lead team initiatives, mentor junior developers, is an excellent team player, has experience collaborating with people across different time zones, and is willing to jump in to help resolve issues quickly. You will be involved in all areas that impact the team, including performance, automation, and development.
The ideal candidate possesses strong analytical, problem‑solving, and consulting skills.
Data Engineering and Foundations
- Design, develop, and optimize scalable ETL pipelines to process large‑scale structured and unstructured datasets.
- Create and manage efficient data architectures that support analytics and machine learning needs.
- Utilize tools like Snowflake and Databricks to deliver high‑performance data solutions.
- Implement rigorous data validation and governance processes to ensure quality and reliability.
- Build and deploy machine learning models for a wide array of use cases.
- Develop deep learning and NLP models using frameworks like Tensor Flow or PyTorch for complex challenges.
- Employ advanced AI methodologies, including transformers, LLMs, and GANs, to address business requirements.
- Design batch and real‑time pipelines to integrate AI‑driven predictions into operational systems.
- Adapt to project requirements by serving as a Data Engineer, Data Scientist, or ML Engineer, leveraging expertise across disciplines.
- Lead the end‑to‑end execution of data and AI initiatives, ensuring alignment with project goals, timelines, and quality standards.
- Continuously optimize workflows to improve performance, scalability, and operational efficiency.
- Engage with stakeholders to understand business objectives and translate them into technical solutions.
- Contribute to cross‑functional team discussions, sharing expertise and best practices in data and AI.
- Stay current with advancements in tools, methodologies, and technologies, applying innovations to improve project outcomes.
- Team player and self‑starter.
- Experience in data engineering, data science, or machine learning roles.
- Strong expertise in Python, SQL, and machine learning frameworks like Tensor Flow or PyTorch.
- Proven ability to design and optimize ETL pipelines and work with platforms like Snowflake or Databricks.
- In‑depth knowledge of machine learning algorithms, including clustering, regression, and time series analysis.
- Hands‑on experience in deploying and maintaining machine learning models in production environments.
- Experience in distributed computing frameworks (PySpark, Hadoop).
- Experience with advanced AI technologies such as transformers, LLMs, and GANs.
- Proven track…
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