Technical Architect AI, Google Cloud Platform

Job Title: Technical Architect AI, Google Cloud Platform

Location: Santa Clara, CA 95054 (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 LLM's & 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 (Azure Functions, Google Cloud Platform Cloud Functions, Kubernetes)
• 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, ensuring scalability, security, and performance
• RAG & Data Pipeline Design (2-3 years) Expertise in designing and optimizing RAG pipelines, including enterprise data ingestion, embedding generation, and vector search using Azure Cognitive Search, Pinecone, Weaviate, FAISS, or Google Cloud Platform Vertex AI Matching Engine
• LLM Optimization & Adaptation (2-3 years) Experience in implementing fine-tuning and parameter-efficient tuning approaches (LoRA, QLoRA, PEFT) and integrating memory modules (long-term, short-term, episodic) to enhance agent intelligence
• Multi-Agent Orchestration (2-3 years) Skilled in designing multi-agent frameworks and orchestration pipelines with LangChain, AutoGen, or DSPy, enabling goal-driven planning, task decomposition, and tool/API invocation
• Performance Engineering (2-3 years) Experience in optimizing Google Cloud Platform Vertex AI models for latency, throughput, and scalability in enterprise-grade deployments
• AI Application Integration (2-3 years) Proven ability to integrate OpenAI and third-party models into enterprise applications via APIs and custom connectors (MuleSoft, Apigee, Azure APIM)
• Governance & Guardrails (1-2 years) Hands-on experience in implementing security, compliance, and governance frameworks for LLM-based applications, including content moderation, data protection, and responsible AI guardrails
• Provide constructive feedback during code reviews and be open to receiving feedback on your own code
• Bachelor's or Master's degree in Computer Science, Data Science, or a related field; Prior experience in working on Agile/Scrum projects with exposure to tools like Jira/Azure DevOps
• Secondary Skills Knowledge of MCP's and A2A SDK; Version Control: Proficiency with Version Control tools like Git; Agile Methodologies - Experience working in Agile development environments

Primary Responsibilities:

• Architect Scalable GenAI Solutions Lead the design of enterprise architectures for LLM and multi-agent systems, ensuring scalability, resilience, and security across Azure and Google Cloud Platform 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, assistants, and RAG pipelines for structured and unstructured data
• Agentic AI Frameworks Define and implement agentic AI architectures leveraging frameworks like LangGraph, AutoGen, DSPy, and cloud-native orchestration tools
• Integration & APIs Oversee integration of OpenAI, Azure OpenAI, and Google Cloud Platform Vertex AI models into enterprise systems, including MuleSoft Apigee connectors
• LLMOps & Governance Establish LLMOps practices (CI/CD, monitoring, optimization, cost control) and enforce responsible AI guardrails (bias detection, prompt injection protection, hallucination reduction)
• Enterprise Governance Lead architecture reviews, governance boards, and technical design authority for all LLM initiatives
• Collaboration Partner with data scientists, engineers, and business teams to translate use cases into scalable, secure solutions
• Documentation & Standards Define and maintain best practices, playbooks, and technical documentation for enterprise adoption
• Monitoring & Observability Guide implementation of AgentOps dashboards for usage, adoption, ingestion health, and platform performance visibility

Secondary Responsibilities:

• Innovation & Research Stay ahead of advancements in OpenAI, Azure AI, and Google Cloud Platform Vertex AI, evaluating new features and approaches for enterprise adoption
• Ecosystem Expertise Remain current on Azure AI services (Cognitive Search, AI Studio, Cognitive Services) and Google Cloud Platform AI stack (Vertex AI, BigQuery, Matching Engine)
• Business Alignment Collaborate with product and business leadership to prioritize high-value AI initiatives with measurable outcomes
• Mentorship Coach engineering teams on LLM solution design, performance tuning, and evaluation techniques
• Proof of Concepts Lead or sponsor PoCs to validate feasibility, ROI, and technical fit for new AI capabilities