Director, AI Platform
United States · Remote · Permanent
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What the market looks like
We've tracked 500+ senior-level postings in AI architecture across the US over the last six months, with strong demand in California, Texas, and New York—particularly in technology, financial services, and professional services. Compensation typically ranges from $250K to $430K annually. The strongest candidates bring hands-on experience architecting AI systems at scale, deep fluency in modern ML infrastructure and deployment patterns, and the ability to translate technical complexity into business value. These leaders often combine deep engineering judgment with the communication skills needed to partner across product, data, and operations teams.
Job responsibilities
Own the technical strategy and roadmap for AI platform capabilities, balancing innovation with reliability and operational cost.
Design and evolve the architecture for model development, training, inference, and monitoring infrastructure to support multiple use cases across the organization.
Lead a team of AI/ML engineers, architects, and platform specialists—setting technical standards, conducting architecture reviews, and mentoring for growth.
Partner with product and business leaders to translate AI opportunities into technical requirements and feasible delivery plans.
Drive decisions on build vs. buy, open-source vs. proprietary, and cloud infrastructure choices—evaluating trade-offs in performance, cost, and time-to-market.
Establish observability, governance, and security practices for AI systems, including model versioning, explainability, bias detection, and compliance frameworks.
Collaborate with data engineering and data science teams to integrate feature pipelines, data quality checks, and retraining workflows into platform operations.
Candidate requirements
10+ years of software engineering or ML systems experience, with at least 4–5 years in AI architecture, platform engineering, or leading ML infrastructure teams.
Demonstrable track record architecting and shipping production AI systems (large language models, computer vision, forecasting, recommender systems, or similar) at meaningful scale.
Deep hands-on knowledge of model serving frameworks, training infrastructure, vector databases, experiment tracking, and monitoring tools in modern ML stacks.
Proven ability to lead and grow engineering teams, set technical vision, and communicate trade-offs to non-technical stakeholders.
Strong judgment on system design—experience with distributed systems, data pipelines, or cloud infrastructure decisions and their business implications.
Familiarity with governance, security, and compliance considerations in AI systems (model governance, bias mitigation, regulatory frameworks).