Technical Architect – AI, GCP

FTE / Contract

Location : Santa Clara, CA

Job 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.
• Architect Scalable GenAI Solutions – Lead the design of enterprise architectures for LLM and multi-agent systems, ensuring scalability, resilience, and security across Azure and GCP 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 GCP 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
• Innovation & Research – Stay ahead of advancements in OpenAI, Azure AI, and GCP 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 GCP 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
• 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, GCP 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 GCP 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 GCP 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 GCP 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