GCP AI Solution Architect
Description
& Summary At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.We are seeking a highly skilled GCP AI Solution Architect with Generative AI expertise and extensive experience working with Vertex AI, Gemini Enterprise, Agent Engine, and Kubeflow pipelines. The ideal candidate will be responsible for designing, developing, and providing guidance on next-generation MLOps and GenAI platforms for our clients, ensuring seamless integration and efficient operation of their machine learning and generative AI models across their environments and teams.
Meaningful work you'll be part of
As a GCP AI Solution Architect with Generative AI specialization, you'll work as part of a team of problem solvers, helping to solve complex business issues, deliver high quality client service and operational efficiency. Responsibilities include but are not limited to:
Design, develop, and troubleshoot MLOps and GenAI infrastructure to support the deployment, monitoring, and management of machine learning, statistical models, andgenerative AI applications.
Implement and manage
Vertex AI, Gemini Enterprise, Agent Engine (ADK - Agent Development Kit), and Kubeflow pipelines to automate model training, validation, and deployment processes for both traditional ML and generative AI workloads.
Build and deploy AI agents using Google's Agent Engine and ADK framework, enabling autonomous task completion and intelligent automation for enterprise clients.
Integrate Gemini Enterprise models into client workflows, including prompt engineering, fine-tuning, grounding, and implementing responsible AI practices.
Collaborate with data scientists, software engineers, and Dev Ops teams to integrate machine learning andgenerative AI workflows into the broader technology ecosystem.
Optimize and troubleshoot machine learning andGenAI pipelines to ensure high performance, cost-efficiency, and reliability in production environments.
Develop and maintain documentation for MLOps processes,GenAI best practices, tools, and implementation guidelines.
Provide recommendations and implement solutions for model monitoring and alerting (training-serving skew, drift detections,hallucination detection, prompt injection vulnerabilities, performance issues, etc.).
Implement safety guardrails and content filtering for generative AI applications to ensure responsible AI deployment.
Stay up-to-date with the latest industry trends and technologies in MLOps,generative AI, and machine learning, especially as it pertains to Vertex AI,Gemini, and Google's Agent ecosystem.
Experiences and skills you'll use to solve
Required
Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
Proven experience as an MLOps Engineer or ML Platform Developer.
Strong proficiency with Vertex AI, Gemini Enterprise, Agent Engine (ADK), and Kubeflow pipelines.
Hands-on experience with generative AI technologies, including large language models (LLMs), prompt engineering, and RAG (Retrieval-Augmented Generation) patterns.
Solid understanding of machine learning lifecycle andgenerative AI deployment best practices.
Experience with Google Cloud Platform (GCP).
Proficiency in Python.
Familiarity with CI/CD tools and practices.
Strong problem-solving skills and attention to detail.
Excellent communication and collaboration skills.
Preferred
Experience with containerization technologies such as Docker and Kubernetes.
Experience with other MLOps tools and frameworks (e.g., MLflow, TFX).
Knowledge of multimodal AI applications (text, image, video generation).
Experience implementing AI agents for autonomous task completion.
Knowledge of data engineering and data pipeline tools (e.g., Apache Beam, Airflow).
Understanding of data governance,AI ethics, and compliance standards.
Experience with monitoring and logging tools (e.g., Prometheus, Grafana).
Familiarity with responsible AI practices, including bias detection, fairness metrics, and model explainability.
Experience with vector databases (e.g., Vertex AI Vector Search, Pinecone) for semantic search applications.
PwC Canada is committed to cultivating an inclusive, hybrid work environment. Exact expectations for your team can be discussed with your interviewer.
Thisposition ensures continuity and upholds our standards of excellence following the departure of a valued team member.
The salary range for this position is $112,400 - $162,400. The posted salary rangerepresentsthe expected hiring range for PwC locations in major citycentres. Given our national recruiting approach, ranges may vary for positions in other locations. At PwC Canada, base salary isdeterminedby your skills, experience,qualificationsand work addition to base salary, eligible employees may have opportunities to participate in variable…
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