Job Role : AI Enterprise Architect

Location : Seattle ,WA (Hybrid)

Job Type : Full time Permanent

Job Description

Must Have Technical/Functional Skills:

Generative AI (Gen AI), Agentic AI

Model selection, evaluation, interpretability: TensorFlow, PyTorch, Hugging Face, NLP, computer vision, time-series modeling.

AI Strategy, Architecture, and Roadmap Planning

Python / R / TypeScript Programming

AI Frameworks (LangChain, AutoGen, Azure AI Foundry, Azure AI Agent, CrewAI, LangGraph, Google ADK)

Model Context Protocol (MCP), Agent to Agent Protocol

N-8-N

GuardRails, AI Ethics and Regulations

Prompt Engineering: “Expertise in prompt engineering, LLM operations, and GenAI deployment best practices.”

Distillation, RAG, Fine-tuning

Multi-modal AI, LLMs

Vector Databases, Embeddings

GenAI deployment tools (Docker, Kubernetes)

AI Solution Assessment and Optimization

ETL, Data Pipelines, Feature Stores: Experience with tools like Airflow, dbt, MLflow, DVC, Azure ML pipelines.

CI/CD for ML: Automated model retraining, versioning, and monitoring.

Data anonymization, privacy-by-design, secure model deployment: Especially for regulated industries (GDPR, SOX, HIPAA).

Desirable certifications: AI/ML, cloud architecture, relevant technology (e.g., GCP GenAI Leader, AWS AI Practitioner, Azure AI certifications)

Roles & Responsibilities:

Regional AI Technical Lead is a strategic technical leader responsible for designing, implementing, and managing advanced generative AI solutions across Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). This role combines deep expertise in generative AI technologies, multi-cloud architecture, and modern software engineering practices to deliver scalable, secure, and innovative AI solutions. The architect leads cross-functional teams, drives technical vision, and ensures alignment between business objectives and AI initiatives.

Ø Strategy & Roadmap

Define and drive the AI strategy, aligning with business goals and innovation priorities.

Develop and maintain the AI solution roadmap, including short-term deliverables and long-term vision.

Evaluate emerging AI trends and technologies to inform strategic direction.

Ø Architecture & Design

Architect end-to-end AI solutions using Gen AI, Agentic AI, LLMs, and multi-modal AI.

Design intelligent agent systems using LangChain, LangGraph, Model Context Protocol (MCP), Agent to Agent Protocols, Google AI Development Kit (ADK), Azure AI Studio or AWS Bedrock.

Integrate large language models (LLMs) such as Llama, Gemini, GPT, and Claude into enterprise systems and custom applications.

Establish scalable and modular AI architectures that support RAG pipelines, Vector DBs (ChromaDB, Pinecone, FAISS, Weaviate, Vertex AI Matching Engine).

Develop and optimize retrieval-augmented generation (RAG) pipelines using vector databases

Define and enforce AI governance frameworks, including Responsible AI, GuardRails, and compliance w ith AI Ethics & Regulations.

Supervise the design, fine-tuning, and optimization of generative AI models and multimodal systems (text, image, audio).

Lead Python-based development for prompt orchestration, tool agents, APIs, and data pipelines.

Ø Assessment & Optimization

Conduct technical assessments of existing AI/ML systems, models, and data pipelines.

Identify gaps, risks, and opportunities for modernization or enhancement.

Recommend architectural improvements and integration strategies for legacy systems.

Ø Cloud Platform Integration & Management

Architect, deploy, and monitor generative AI solutions on GCP (Vertex AI, Document AI, AlloyDB, BigQuery, Cloud Run), Azure (OpenAI Service, Cognitive Search, Azure ML, Azure Functions), and AWS (Bedrock, SageMaker, Lambda, API Gateway, DynamoDB).

Design and manage scalable cloud infrastructure, ensuring performance, cost efficiency, and compliance across platforms.

Implement containerization and orchestration strategies using Docker and Kubernetes (GKE/EKS/AKS) for reliable deployment.

Establish and enforce security frameworks using GCP IAM, Azure Identity, AWS IAM, and related tools for secure, compliant access.

Utilize monitoring and logging solutions such as Google Cloud Operations Suite, Azure Monitor, and AWS CloudWatch.

Ø MLOps, DevOps & Governance

Automate model deployment, versioning, and monitoring using MLOps/DevOps best practices and CI/CD pipelines.

Implement prompt optimization, context management, and model performance tuning.

Ensure adherence to data governance, privacy, PII handling, and AI ethics principles throughout the development lifecycle.

Ø Leadership & Collaboration

Collaborate with product owners, data scientists, engineers, and business stakeholders.

Mentor engineering teams and contribute to talent development in AI and ML domains.

Represent AI architecture in enterprise governance forums and technical councils.

Required Skills

Performance Architect

Are you looking for more jobs nearby? Find your favorite jobs now by visiting our online jobs page.