Software Architect: - AI Technical Advocate

IBM - San Jose, CA

Full-time

San Jose, CA

Computer and Electronic Product Manufacturing

About the position

The AI Technical Advocate role is designed for a Software Architect who will collaborate with software development and sales teams to promote and educate clients on the enterprise AI features of the company. This position requires a deep understanding of AI technologies and the ability to communicate complex technical concepts effectively. The advocate will play a crucial role in shaping the architecture of the AI portfolio and ensuring that client challenges are met with innovative AI solutions.

Responsibilities

• Architectural Leadership: Work collaboratively within a team responsible for shaping the architecture and technical trajectory of our AI portfolio.
• Domain Expertise: Possess a deep understanding of AI technologies and innovations including the company's offerings and the competitive landscape.
• Collaboration and Adaptability: Collaborate seamlessly with diverse teams and contribute to other product initiatives.
• Effective Communication: Utilize strong communication skills to articulate complex technical concepts, fostering collaboration and understanding across interdisciplinary teams.
• Technical Solution Workshops: Conducting and participating in technical solution workshops.

Requirements

• Deep expertise in AI/ML model development and/or deployment of AI/ML workloads including experience in LLMs, Vector DBs, model tuning, LLM benchmarking.
• Experience with building credibility for Data and AI methodology and practices for data management, data fabric, data governance, trustworthy AI and business analytics or equivalent enterprise solutions.
• Proficient in Python programming, NLP techniques, and AI Frameworks (e.g., Hugging Face).

Nice-to-haves

• Architecting and deploying scalable AI solutions that integrate seamlessly with existing business and IT infrastructure.
• Experience designing & developing automated pipelines for data extraction, ML model training, ML model production deployments and production monitoring.
• Awareness of ethical considerations in data science and AI to ensure responsible data usage.