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Leads AI initiatives from concept through deployment, adoption, and ownership. Translates priorities into technical plans, coordinates dependencies across engineering and product teams, drives decisions, manages risks, defines evaluations, and ensures deployment readiness. Establishes support and monitoring ownership, partners with security and governance teams, and converts project learnings into reusable delivery standards.

Lead AI initiatives from concept through deployment and adoption. Translate priorities into technical plans, coordinate dependencies across engineering and business teams, drive decisions, manage risks, oversee evaluations and deployment readiness, establish support ownership, and ensure solutions deliver measurable outcomes. The role also requires hands-on technical fluency with Python, SQL, APIs, LLM SDKs, data pipelines, agent systems, and evaluation practices.

Lead cross-functional AI technical programs from prioritization through deployment and adoption. Translate initiatives into plans, requirements, milestones, evaluations, and measurable outcomes; coordinate Engineering, Product, Data, FDE, AI Enterprise, and platform teams; manage dependencies, risks, governance, deployment readiness, and operational ownership. Prototype and debug AI integrations using Python, SQL, APIs, and LLM SDKs while evaluating quality, latency, cost, and permissions.

Leads AI initiatives from concept through deployment, adoption, and ownership. Responsibilities include creating technical plans, coordinating dependencies across engineering and business teams, driving technical decisions, managing risks and blockers, coordinating evaluations and deployment readiness, establishing support and monitoring ownership, and ensuring AI governance reviews. The role requires hands-on technical fluency, LLM and agent evaluation knowledge, operational rigor, and strong cross-functional communication.

Leads AI initiatives from concept through deployment and adoption. Develops technical plans, coordinates dependencies across engineering and product teams, drives decisions, manages execution risks, oversees evaluations and deployment readiness, and establishes ownership for monitoring and maintenance. Requires hands-on fluency with Python, SQL, APIs, LLM SDKs, data systems, agent orchestration, testing, and evaluation. Partners with AI security and governance teams in a fintech environment.

Drive AI initiatives from concept through deployment, adoption, and ownership. Translate priorities into technical plans, define milestones and success metrics, coordinate cross-functional dependencies, guide technical decisions, manage risks and blockers, oversee evaluations and deployment readiness, and establish support and monitoring ownership. The role requires hands-on technical fluency, agent and evaluation expertise, operational rigor, and strong communication across business and engineering teams.

Leads AI initiatives from prioritization through deployment, adoption, and ownership. Responsibilities include creating technical plans, coordinating cross-functional dependencies, driving engineering decisions, managing execution risks, coordinating evaluations and deployment readiness, establishing support ownership, and partnering on AI security and governance reviews. The role requires hands-on technical fluency, LLM and agent evaluation knowledge, operational rigor, and strong cross-functional communication.

Build and debug LLM applications, agents, integrations, and workflows using Python and APIs. Provide technical office hours, mentoring, code reviews, and debugging support to colleagues. Develop reusable agent frameworks, starter kits, evaluations, documentation, and learning resources. Teach hands-on AI bootcamps, support adoption, troubleshoot failures, and escalate complex data, security, platform, or production issues to specialists.

Build and debug LLM agents, integrations, and AI workflows using Python and APIs. Provide technical office hours, mentoring, code reviews, debugging support, and hands-on training. Develop reusable starter kits, frameworks, evaluations, and troubleshooting guides on approved platforms such as Maestro. Diagnose issues involving prompts, retrieval, tool calls, state, permissions, and failures, escalating complex production or security concerns to specialist teams.

Build and debug LLM agents, integrations, and AI workflows using Python and APIs. Provide technical office hours, mentoring, code reviews, and troubleshooting support; create reusable frameworks, starter kits, evaluations, documentation, and learning programs. Partner with AI, data, security, and platform teams to resolve escalations, improve safeguards, and increase independent adoption of approved AI tools.