Director, Enterprise AI Platform Architect
New
J
JobgetherEnterprise AI
Based in United StatesFull-TimeDirector
Salary$200,000–$315,000
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Job Details
- Experience
- At least 3 years of experience in AI/ML platform architecture and development, including 2+ years of recent hands-on experience with generative AI
- Required Skills
- AWSGCPMachine LearningAzureDeep LearningGenerative AI
Requirements
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related technical discipline.
- At least 3 years of experience in AI/ML platform architecture and development.
- 2+ years of recent hands-on experience with generative AI and agentic architectures in production applications.
- Proven track record of delivering AI applications into production environments.
- Strong expertise in LLMs, RAG architectures, diffusion models, prompt engineering, and model fine-tuning.
- Deep understanding of autonomous agents, multi-agent systems, and enterprise AI patterns.
- Extensive knowledge of public cloud AI services (AWS, Azure, and GCP).
- Experience designing platform abstraction layers to minimize vendor dependency.
- Ability to operate effectively in fast-paced, ambiguous environments.
- Strong analytical, problem-solving, and architectural decision-making skills.
- Excellent communication and interpersonal skills for executive-level presentations.
- Experience in private equity portfolio companies or consulting environments is strongly preferred.
Responsibilities
- Partner with technology and product teams to identify and prioritize agentic AI opportunities aligned with business objectives.
- Lead end-to-end delivery of AI agent solutions using rapid development approaches to achieve measurable outcomes.
- Guide AI solutions from prototype through production deployment and establish scalable operating models.
- Conduct AI maturity assessments and develop tailored improvement roadmaps.
- Design enterprise AI platforms across AWS, Azure, and GCP with a focus on vendor independence.
- Develop and maintain reference architectures for generative AI, agentic systems, and multi-agent collaboration.
- Define enterprise-wide standards for AI development, deployment, lifecycle management, and governance.
- Provide technical due diligence assessments regarding AI capabilities and technical debt.
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