Gen AI Architect

  • Santa Clara, CA
  • Posted 1 day ago | Updated 15 hours ago

Overview

On Site
Accepts corp to corp applications
Contract - W2

Skills

Governance
Enterprise Architecture
Requirements Analysis
Artificial Intelligence
MuleSoft
Technical Documentation
BigQuery
Performance Tuning
Microsoft Azure
Unstructured Data
Mentoring
scalability
APIGEE
Application Programming Interfaces (APIs)
Leadership
Continuous Integration
Team Working
Safety Principles
Large Language Models
Architecture
Dashboards
Innovation
Technical Management
Engineering Design Process
Machine Learning Operations
Management Accounting
Business Alignment
Generative AI
Multi-Agent Systems
Perseverance
Technology Strategies

Job Details

Job Title: Gen AI Architect

Location: Santa Clara, CA

Duration: Contract

Need 14+ years of experience resume.

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 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 Lang Graph, 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 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 Google Cloud Platform 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 Google Cloud Platform 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.

Thanks & Regards

Akhil

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