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Metro location, strong brand, and broad AI skillset attract applicants, but senior, specialized VP role reduces density.
High because regulated banking, model-risk, and safety requirements make cross-industry transfer harder.
Many mandatory filters: VP leadership, production generative AI, LLM integrations, cloud, data pipelines, and regulated experience.
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Lead and manage a team of AI engineers responsible for designing, building, and operating production-scale generative and agentic AI systems impacting millions of users.
Own the technical vision and architectural decisions for AI systems including multi-agent workflows, RAG pipelines, LLM integrations, data pipelines, and cloud-native services on AWS.
Ensure AI system reliability, safety, and optimization by implementing guardrails, observability, and cost/latency improvements while collaborating with senior stakeholders across business and compliance functions.
Work Experience Required: Deep hands-on experience shipping production AI/ML or generative AI systems at scale in high-stakes or regulated environments.
Strong proficiency in Python, including async programming for agent workflows and API integrations.
Experience with frontline LLM providers (e.g., OpenAI, Anthropic), RAG architectures, vector stores, Apache Airflow, Snowflake, and AWS cloud-native AI platforms.
Experience building and leading teams (leadership at VP level implied).
Experienced leader capable of both strategic technical vision and hands-on coding in a high-complexity AI environment.
Expertise in integrating multiple frontier LLMs and designing robust, scalable AI architectures with strong data engineering skills.
Comfortable working in regulated or sensitive financial-service contexts and collaborating cross-functionally to align AI delivery with business and compliance priorities.