Artificial Intelligence Engineer

AI Developer Job summary An Agentic AI developer specializing in Google Cloud Platform (GCP) is a visionary leader responsible for designing and deploying intelligent, autonomous systems using Google's AI stack and the Google Cloud AI platform, including generative AI, multi-agent frameworks like the Agent Development Kit (ADK, and cloud AI infrastructure on Google Cloud. This role involves defining the architecture for autonomous AI agents capable of reasoning, planning, learning, and acting in complex environments to achieve goals. The architect works with product managers, AI engineers, data scientists, and business stakeholders to create scalable and ethical agentic AI solutions on GCP. Essential responsibilities Key responsibilities include: Defining the architectural vision and strategy for agentic AI solutions, leveraging GCP services for optimal performance and cost-effectiveness. Designing end-to-end architectures for agentic AI systems, including generative AI model integration and utilizing Vertex AI Agent Builder for streamlined development. Guiding the integration of agentic AI solutions with existing systems, leveraging APIs, Apigee, and data connectors for seamless interoperability across the Google Cloud ecosystem. Establishing architectural patterns and best practices for building scalable and reliable agentic AI on GCP, considering services like Google Kubernetes Engine (GKE) and Compute Engine. Collaborating with engineering and product teams to ensure architectural alignment and provide guidance on GCP-specific considerations. Evaluating emerging AI and agentic technologies and their potential architectural impact within the Google Cloud ecosystem. Overseeing the design and implementation of data pipelines and infrastructure required for agentic AI, leveraging GCP services like BigQuery and Cloud Storage. Architecting and implementing agentic AI systems leveraging GCP services (Vertex AI, BigQuery, Cloud Functions, Pub/Sub, etc.). Staying updated on AI advancements and best practices within the Google Cloud environment. Ensuring enterprise-grade security for AI agents, including identity controls, secure perimeters, and data governance tools. Qualifications Typically, a bachelor's or master's degree in a relevant field like computer science or AI is required. Experience in AI/ML systems architecture, particularly in agentic or autonomous AI, on Google Cloud Platform is highly desirable. Experience in designing robust data architectures and prior internal consulting experience are also beneficial. A minimum of three years of industry experience, including one year of hands-on experience with Google Cloud Platform, is generally recommended. Skills Essential skills include: Strong knowledge of Large Language Models (LLMs) and their application in agentic systems, particularly within the Vertex AI ecosystem. Understanding of agentic AI concepts (planning, reasoning, memory, action) and expertise in building multi-agent systems using frameworks like ADK, LangChain, or LangGraph. Software architecture principles and experience designing APIs and microservices on GCP. Proficiency in Python for building large, scalable applications on GCP. Experience with prompt engineering for optimizing LLM performance and leveraging Vertex AI's grounding capabilities, such as Vertex AI Search and Vector Search. Familiarity with MLOps best practices and experience with GCP MLOps tools within Vertex AI. Excellent communication, problem-solving, analytical, leadership, and strategic thinking skills. Experience with GCP services like Vertex AI, BigQuery, Cloud Functions, Pub/Sub, and Cloud Storage. Strong understanding of GCP security and compliance features for AI/ML deployments. Experience Desired experience includes: Success in creating agentic workplaces or significant AI enablement work on GCP. Experience with organizational change management for technology adoption on Google Cloud. Ability to translate technical concepts into business value and clearly communicate with both technical and non-technical stakeholders. Experience with the Google Cloud Professional Machine Learning Engineer certification or equivalent demonstrates a deep understanding of building and managing ML models on GCP.