Gen AI Architect

Job Title: Gen AI Architect

Location: Santa Clara, CA

Duration: Contract

Need 14+ years of experience resume.

Accepts only H1b Visa

As a Technical Architect specializing in LLMs and Agentic AI, you will own the architecture, strategy, and delivery of enterprise-grade AI solutions. You will 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 Lang Graph, 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 Agen tops 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.

• Proof of Concepts: Lead or sponsor PoCs to validate feasibility, ROI, and technical fit for new AI capabilities.

• Ecosystem Expertise: Remain current on Azure AI services (Cognitive Search, AI Studio, Cognitive Services) and GCP AI stack (Vertex AI, Big Query, 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.