Engineering Director, Google Cloud

PwC•Tampa, FL

2d•$155,000 - $410,000

About The Position

The Engineering Director, Google Cloud will lead cloud-native application design, development, testing, and modernization efforts across Google Cloud programs within PwC’s Advisory practice. The Director sets direction across multiple workstreams, shapes client engagements, supports business growth, and guides teams through technical choices, delivery tradeoffs, and changing client needs while promoting quality, inclusion, and integrity. The role combines software engineering, data and analytics, and Generative AI capabilities to help clients build scalable solutions and modernize legacy applications. It oversees work from concept and architecture through build, testing, and deployment across technologies and delivery settings. The position also involves leading client discussions, advising on architecture and modernization decisions, developing AI solutions, and building client relationships and engineering team leadership.

Requirements

At least a Bachelor's degree.
At least 9 years of experience.

Nice To Haves

Experience leading Google Cloud native development and testing initiatives.
Experience building cloud-native applications across full development lifecycles.
Experience applying Generative AI, LLMOps, and prompt engineering.
Experience using Python, LangChain, LangGraph, and Vertex AI.
Experience driving client-facing solution design and business growth.
Experience mentoring teams through rapid experimentation and innovation.

Responsibilities

Set strategic direction for Google Cloud-native engineering programs across AI-enabled application development and testing.
Lead client discussions to shape solution roadmaps, delivery priorities, and modernization approaches for cloud-native products.
Oversee multiple software engineering efforts from requirements through architecture, build, testing, and deployment.
Drive development of generative AI solutions using Google Vertex AI, LLM APIs, LangChain, LangGraph, and retrieval-augmented generation.
Govern prompt engineering, LLM evaluation, LLM guardrails, and LLMOps practices to support reliable solution behavior.
Translate business needs into scalable cloud-native applications using Python, agent orchestration, vector databases, and digital tooling.
Advance AI-human collaboration patterns for contact center and enterprise use cases through rapid experimentation and iterative design.
Advise on architecture decisions that balance performance, maintainability, and modernization across cloud and software platforms.
Build executive-level client relationships while identifying new opportunities for cloud engineering and generative AI programs.
Coach delivery teams and cultivate technical leadership across engineering, testing, and AI solution design.