Jobs via Dice
Technical Architect AI, Google Cloud Platform
Jobs via Dice, Santa Clara, California, us, 95053
Technical Architect AI, Google Cloud Platform
Location: Santa Clara, CA (Onsite). FTE position. Job / Role Description: As a Technical Architect specializing in LLMs and Agentic AI, you will own the architecture, strategy, and delivery of Enterprise-grade AI solutions. Work with cross-functional teams and customers to define the AI roadmap, design scalable solutions, and ensure responsible deployment of Generative AI across the organization. Skills / Experience
Experience 10+ years of experience in AI/ML-related roles, with a strong focus on LLMs & Agentic AI technology Generative AI Solution Architecture (2-3 years). Proven experience in designing and architecting GenAI applications, including Retrieval-Augmented Generation (RAG), LLM orchestration (LangChain, LangGraph), and advanced prompt design strategies Backend & Integration Expertise (5+ years). Strong background in architecting Python-based Microservices, APIs, and orchestration layers that enable tool invocation, context management, and task decomposition across cloud-native environments Enterprise LLM Architecture (2-3 years). Hands-on experience in architecting end-to-end LLM solutions using Azure OpenAI, Azure AI Studio, Hugging Face models, and Google Cloud Platform Vertex AI RAG & Data Pipeline Design (2-3 years). Expertise in designing and optimizing RAG pipelines, including enterprise data ingestion, embedding generation, and vector search using multiple platforms LLM Optimization & Adaptation (2-3 years). Experience in implementing fine-tuning and parameter-efficient tuning approaches and integrating memory modules to enhance agent intelligence Multi-Agent Orchestration (2-3 years). Skilled in designing multi-agent frameworks and orchestration pipelines with LangChain, AutoGen, or similar tools Performance Engineering (2-3 years). Experience in optimizing Vertex AI models for latency, throughput, and scalability AI Application Integration (2-3 years). Proven ability to integrate OpenAI and third-party models into enterprise applications via APIs and connectors Governance & Guardrails (1-2 years). Experience in implementing security, compliance, and governance frameworks for LLM-based applications, including content moderation and responsible AI guardrails Education: Bachelors or Masters degree in Computer Science, Data Science, or related field; experience with Agile/Scrum tools (Jira/Azure DevOps) Secondary Skills: Knowledge of MCPs and A2A SDK; Version Control (Git); Agile methodologies Primary Responsibilities
Architect Scalable GenAI Solutions. Lead the design of enterprise architectures for LLM and multi-agent systems, ensuring scalability, resilience, and security across cloud platforms Technology Strategy & Guidance. Provide strategic technical leadership to customers and internal teams, aligning GenAI projects with business outcomes LLM & RAG Applications. Architect and guide development of LLM-powered applications and RAG pipelines for structured and unstructured data Agentic AI Frameworks. Define and implement agentic AI architectures using frameworks like LangGraph, AutoGen, DSPy Integration & APIs. Oversee integration of OpenAI, Azure OpenAI, and Vertex AI models into enterprise systems LLMOps & Governance. Establish LLMOps practices (CI/CD, monitoring, optimization, cost control) and enforce responsible AI guardrails Enterprise Governance. Lead architecture reviews and governance for all LLM initiatives Collaboration. Partner with data scientists, engineers, and business teams to translate use cases into scalable solutions Documentation & Standards. Define and maintain best practices and technical documentation Monitoring & Observability. Guide implementation of dashboards for usage, health, and performance Secondary Responsibilities
Innovation & Research. Stay ahead of advancements in OpenAI, Azure AI, and Google Vertex AI Ecosystem Expertise. Remain current on Azure AI services and Google Cloud Platform AI stack Business Alignment. Collaborate with product and business leadership to prioritize AI initiatives Mentorship. Coach engineering teams on LLM solution design and evaluation Proofs of Concepts. Lead or sponsor PoCs to validate feasibility and ROI Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology Software Development
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Location: Santa Clara, CA (Onsite). FTE position. Job / Role Description: As a Technical Architect specializing in LLMs and Agentic AI, you will own the architecture, strategy, and delivery of Enterprise-grade AI solutions. Work with cross-functional teams and customers to define the AI roadmap, design scalable solutions, and ensure responsible deployment of Generative AI across the organization. Skills / Experience
Experience 10+ years of experience in AI/ML-related roles, with a strong focus on LLMs & Agentic AI technology Generative AI Solution Architecture (2-3 years). Proven experience in designing and architecting GenAI applications, including Retrieval-Augmented Generation (RAG), LLM orchestration (LangChain, LangGraph), and advanced prompt design strategies Backend & Integration Expertise (5+ years). Strong background in architecting Python-based Microservices, APIs, and orchestration layers that enable tool invocation, context management, and task decomposition across cloud-native environments Enterprise LLM Architecture (2-3 years). Hands-on experience in architecting end-to-end LLM solutions using Azure OpenAI, Azure AI Studio, Hugging Face models, and Google Cloud Platform Vertex AI RAG & Data Pipeline Design (2-3 years). Expertise in designing and optimizing RAG pipelines, including enterprise data ingestion, embedding generation, and vector search using multiple platforms LLM Optimization & Adaptation (2-3 years). Experience in implementing fine-tuning and parameter-efficient tuning approaches and integrating memory modules to enhance agent intelligence Multi-Agent Orchestration (2-3 years). Skilled in designing multi-agent frameworks and orchestration pipelines with LangChain, AutoGen, or similar tools Performance Engineering (2-3 years). Experience in optimizing Vertex AI models for latency, throughput, and scalability AI Application Integration (2-3 years). Proven ability to integrate OpenAI and third-party models into enterprise applications via APIs and connectors Governance & Guardrails (1-2 years). Experience in implementing security, compliance, and governance frameworks for LLM-based applications, including content moderation and responsible AI guardrails Education: Bachelors or Masters degree in Computer Science, Data Science, or related field; experience with Agile/Scrum tools (Jira/Azure DevOps) Secondary Skills: Knowledge of MCPs and A2A SDK; Version Control (Git); Agile methodologies Primary Responsibilities
Architect Scalable GenAI Solutions. Lead the design of enterprise architectures for LLM and multi-agent systems, ensuring scalability, resilience, and security across cloud platforms Technology Strategy & Guidance. Provide strategic technical leadership to customers and internal teams, aligning GenAI projects with business outcomes LLM & RAG Applications. Architect and guide development of LLM-powered applications and RAG pipelines for structured and unstructured data Agentic AI Frameworks. Define and implement agentic AI architectures using frameworks like LangGraph, AutoGen, DSPy Integration & APIs. Oversee integration of OpenAI, Azure OpenAI, and Vertex AI models into enterprise systems LLMOps & Governance. Establish LLMOps practices (CI/CD, monitoring, optimization, cost control) and enforce responsible AI guardrails Enterprise Governance. Lead architecture reviews and governance for all LLM initiatives Collaboration. Partner with data scientists, engineers, and business teams to translate use cases into scalable solutions Documentation & Standards. Define and maintain best practices and technical documentation Monitoring & Observability. Guide implementation of dashboards for usage, health, and performance Secondary Responsibilities
Innovation & Research. Stay ahead of advancements in OpenAI, Azure AI, and Google Vertex AI Ecosystem Expertise. Remain current on Azure AI services and Google Cloud Platform AI stack Business Alignment. Collaborate with product and business leadership to prioritize AI initiatives Mentorship. Coach engineering teams on LLM solution design and evaluation Proofs of Concepts. Lead or sponsor PoCs to validate feasibility and ROI Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology Software Development
#J-18808-Ljbffr