Why This Role Stands Out
You can make a significant impact by architecting cutting-edge Agentic AI solutions within a reputable company, leveraging your Google Cloud AI/ML expertise. This hybrid role is ideal for driven mid-senior professionals eager to pioneer autonomous AI systems and advance their skills in a collaborative environment. Apply now to shape the future of AI integration in CRM and BPM!
Quick Overview
Job Description
Job Description:
We are looking for a senior Enterprise/AI Architect with hands-on experience designing Agentic AI / Generative AI solutions for CRM and BPM environments.
The candidate should have strong Google Cloud (Google Cloud Platform) AI/ML expertise, experience building autonomous or multi-agent systems, and the ability to integrate AI agents across the software development and testing lifecycle.
Key Responsibilities
- Architect and design Agentic AI solutions for CRM and BPM platforms across the SDLC — from requirements → backlog → development → testing → deployment.
- Design and lead development of autonomous AI agents capable of reasoning, decision-making, collaboration, and task execution across the software testing lifecycle.
- Build and optimize AI/ML pipelines using Google Cloud Platform, particularly Vertex AI, BigQuery, Cloud Functions, and related services.
- Integrate AI agents with CI/CD pipelines, test management tools, test automation frameworks, and developer environments.
- Deploy and orchestrate AI agents using Google AgentSpace or comparable agent orchestration platforms.
- Design multi-agent workflows and establish patterns for agent communication, lifecycle management, scalability, and monitoring.
- Work closely with QA, DevOps, Product Engineering, and business stakeholders to identify and implement AI-driven automation opportunities.
- Establish standards for AI safety, governance, interpretability, observability, security, and performance.
- Evaluate emerging technologies in LLMs, Generative AI, multi-agent systems, and autonomous software engineering.
- Provide architecture guidance, technical leadership, design documentation, and best practices to engineering teams.
Required Qualifications – Must Have
- Agentic / Generative AI
- Proven experience as an AI Architect, ML Engineer, Solution Architect, or Enterprise Architect.
- 2+ years of hands-on AI/ML experience, including 1+ year focused on Generative AI / Agentic AI.
- Hands-on experience designing or implementing AI agents, autonomous workflows, or multi-agent systems.
- Strong understanding of LLMs, RAG, prompt engineering, tool/function calling, agent orchestration, and multi-agent architectures.
- Google Cloud Platform / Google AI
- Strong hands-on Google Cloud Platform experience, particularly within the AI/ML ecosystem.
- Experience with Vertex AI, BigQuery, Cloud Functions, and related Google Cloud Platform services.
- Experience building, deploying, or managing AI solutions on Google Cloud Platform.
- CRM & BPM
- 3+ years of hands-on experience developing or working with CRM and BPM solutions.
- Ability to understand and architect AI use cases within enterprise CRM/BPM workflows.
- Testing & SDLC
- Strong understanding of the software development and testing lifecycle.
- Experience with test automation frameworks/tools such as Selenium, TestNG, JUnit, or equivalent.
- Experience integrating AI agents into QA/testing and CI/CD processes.
- Agent Deployment / Orchestration
- Experience deploying and managing agents using Google AgentSpace or a comparable agent orchestration/agent platform.
- Understanding of agent lifecycle management, communication, scalability, monitoring, and governance.
- Architecture & Leadership
- Strong enterprise architecture and solution-design experience.
- Excellent communication, problem-solving, stakeholder-management, and technical leadership skills.
Nice to Have
- Direct experience with Google AgentSpace.
- Experience with Gemini / Vertex AI Agent Builder / Agent Engine.
- Salesforce, Microsoft Dynamics, Pega, Appian, Camunda, or similar CRM/BPM platforms.
- Experience building AI-powered software testing or autonomous QA agents.
- Experience with AI governance, evaluation frameworks, safety, and responsible AI.
- Experience designing enterprise-scale multi-agent architectures.
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