Technical Architect AI, GCP

Dear Team

Please support for the below role.

Technical Architect AI GCP

FTE / Contract with WinWire

Santa Clara CA 95054 (Onsite)

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 (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
• Bachelors or Masters 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

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.

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 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

Secondary Responsibilities

• 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

OTHERS/SKILLS

• Communicate effectively with internal and customer stakeholders; Communication approach: verbal emails and instant messages
• Strong interpersonal skills to build and maintain productive relationships with team members & customer representatives
• Provide constructive feedback during code reviews and be open to receiving feedback on your own code
• Problem-Solving and Analytical Thinking; Capability to troubleshoot and resolve issues efficiently
• Ability to bring idea into reality through technology implementation & adoption
• Provides regular updates proactive and due diligent to carry out responsibilities