Google Cloud Generative AI Solution Lead Senior Manager

Responsibilities:

• Strategic Solutioning & Architecture: Lead the translation of complex client business challenges into innovative Generative AI and Agentic solutions. Define technical strategy, solution designs, and comprehensive roadmaps, ensuring alignment with business objectives and delivering significant client value.
• Generative AI & Agentic AI: Provide architectural guidance and technical leadership for the design, development, and deployment of robust GenAI applications and intelligent agents on GCP. Drive the strategic selection and implementation of Agentic frameworks (e.g., Agent Development Kit, Gemini Enterprise / Agentspace, LangGraph, Langchain etc.).
• GCP Ecosystem Mastery: Architect and optimize the strategic utilization of Google Cloud services including Vertex AI (Studio, Model Garden, Pipelines, Endpoints), Vector Search, Gemini Models, Document AI, Contact Center AI (CCAI). Leverage GCP for scalability, security, and reliability through GKE, Cloud Run, Cloud Functions, BigQuery, and Composer.
• Advanced Application Engineering: Oversee and provide technical leadership for building scalable Python-based back-end APIs (FastAPI, Flask etc.) and integrating with front-end applications (React.js). Define API architectural patterns for robust and secure GenAI-driven applications.
• Data & MLOps Strategy: Define and implement data ingestion, preparation, and management strategies for GenAI models. Establish and enforce best practices for CI/CD pipelines and MLOps within GCP (e.g., Vertex AI Pipelines) for automated and observable deployments.
• Own delivery across GenAI Engagements: supporting multiple teams, accountable for quality, commercial outcomes, and client satisfaction. Lead and develop Managers and senior engineers.
• Client & Stakeholder Engagement: Act as a key technical liaison, clearly articulating complex technical concepts to both technical teams and senior business leadership. Drive alignment, mentor junior team members, and foster a collaborative environment.
• Innovation & Thought Leadership: Serve as a subject matter expert in Generative AI and Agentic development, continuously evaluating emerging technologies and contributing to PwC's intellectual property and industry thought leadership - including reusable accelerators, golden reference architectures, delivery playbooks, and hiring and capability development.
• AI Governance & Responsible AI: Lead the implementation of AI governance frameworks, including the design and integration of LLM guardrails, ensuring solutions adhere to ethical AI principles, data privacy, compliance, and PWC's robust quality and security standards.
• Shape pursuits, proposals, and statements of work: Build trusted relationships with senior client stakeholders, and identify and convert new opportunities to grow the practice

Requirements:

• Experience Profile:
• 10+ years of progressive experience in software, data, or ML engineering, including significant technical leadership
• 6+ years hands-on experience with Google Cloud Platform (GCP).
• 4+ years leading Generative AI or Agentic delivery at scale, across multiple teams or concurrent engagements
• 2+ years leading Generative AI or Agentic AI development projects from conception to production.
• Technical Expertise:
• Strong hands-on foundation in Python and cloud-native development, sufficient to review designs and code, guide teams, and make deep technical calls.
• Working familiarity with common back-end frameworks and the Google Cloud Python SDK to guide implementation choices.
• Familiarity with React.js for front-end integration.
• Strong SQL and data modeling foundation to guide data and warehousing design
• Comprehensive understanding of data warehousing, data lakes, and data mesh concepts.
• Designs end-to-end GenAI and multi-agent architectures across data, models, orchestration, integration, and security, balancing scalability, reliability, cost, and compliance.
• Generative AI & Agentic Proficiency:
• Defines agentic architecture patterns and framework selection standards across engagements, with deep experience in ADK, Gemini Enterprise / Agentspace, LangGraph, and Langchain.
• Proven experience designing and implementing Retrieval Augmented Generation (RAG) systems.
• Establishes prompt engineering, prompt chaining, and evaluation standards for the practice
• Experience with Agent-to-Agent (A2A) communication patterns and leading LLM frameworks.
• Hands-on experience in designing telemetry event schemas, log analysis pipelines, and building operational KPI dashboards for Generative AI systems.
• Architectural experience with stateful multi-agent systems, long-term memory persistence and Human-in-the-loop (HITL) safety gates.
• Direct experience designing and implementing LLM guardrails (e.g., content moderation, safety filters, hallucination mitigation) and ensuring AI system adherence to governance policies.
• GCP Architectural Skills:
• Advanced architectural understanding and hands-on experience with Vertex AI, Vector Search, Gemini Models, Document AI, Contact Center AI (CCAI).
• Expertise in GCP containerization (GKE, Cloud Run), serverless (Cloud Functions), and data services (BigQuery, Cloud Storage, Composer).
• Defines MLOps standards and reference pipelines for GenAI delivery on GCP.