AI Architect

Suzega; United States; Remote; USD 180,000 - 280,000; Full time

Job summary

Suzega seeks a seasoned AI Architecture Leader to shape enterprise AI strategy, design scalable ML systems, and guide governance and responsible deployment. You will define architecture standards, mentor teams, and steer pilots across industries.

Expertise in ML frameworks and LLMs, plus experience with major cloud AI services and retrieval-augmented generation, is essential for this pivotal role.

Qualifications

• Deep expertise with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, and Hugging Face.
• Extensive hands-on experience with large language models and generative AI technologies.
• Strong knowledge of leading AI platforms, including OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock, and Google Vertex AI.
• Proven track record architecting and operating production-grade AI systems at scale.
• Experience with MLOps tooling such as MLflow, Kubeflow, Weights & Biases, or Neptune.ai.
• Deep familiarity with vector databases and retrieval systems, including Pinecone, Weaviate, Chroma, or FAISS.
• Strong grasp of cloud platforms and their AI/ML services, plus prompt engineering, retrieval-augmented generation, and fine-tuning techniques.
• Understanding of responsible AI principles, data privacy, and model security considerations.

Responsibilities

• Design end-to-end AI and machine learning architectures spanning model selection, infrastructure, deployment patterns, and integration into existing enterprise systems.
• Lead technical strategy for implementations, including proofs of concept and pilot programs that validate architectural choices before scale-out.
• Architect scalable ML pipelines, MLOps frameworks, and model governance systems that cover versioning, monitoring, and continuous improvement.
• Evaluate and recommend AI platforms, frameworks, and tools across major providers, including Azure AI, AWS SageMaker, and Google Vertex AI.
• Design supporting data architectures such as data lakes and feature stores, plus vector database structures for retrieval workloads.
• Develop AI governance frameworks covering ethical practices, bias mitigation, explainability, and responsible deployment.
• Mentor engineering teams on AI/ML architecture principles and emerging technologies.

Skills

TensorFlow

PyTorch

scikit-learn

Hugging Face

Large language models

Generative AI

OpenAI platform

Anthropic Claude

Azure OpenAI

AWS Bedrock

Google Vertex AI

MLOps tools

MLflow

Kubeflow

Weights & Biases

Neptune.ai

Vector databases

Pinecone

Weaviate

Chroma

FAISS

Prompt engineering

Tools

MLflow

Kubeflow

Weights & Biases

Neptune.ai

Job description

Role overview

Shape the technical direction for enterprise-grade artificial intelligence initiatives, defining architecture standards, governance practices, and the rationale for safe, responsible build decisions. The position blends hands-on architectural design with strategic leadership across diverse industries and use cases.

Responsibilities

• Design end-to-end AI and machine learning architectures spanning model selection, infrastructure, deployment patterns, and integration into existing enterprise systems.
• Lead technical strategy for implementations, including proofs of concept and pilot programs that validate architectural choices before scale-out.
• Architect scalable ML pipelines, MLOps frameworks, and model governance systems that cover versioning, monitoring, and continuous improvement.
• Evaluate and recommend AI platforms, frameworks, and tools across major providers, including Azure AI, AWS SageMaker, and Google Vertex AI.
• Design supporting data architectures such as data lakes and feature stores, plus vector database structures for retrieval workloads.
• Develop AI governance frameworks covering ethical practices, bias mitigation, explainability, and responsible deployment.
• Mentor engineering teams on AI/ML architecture principles and emerging technologies.

Requirements

• Deep expertise with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, and Hugging Face.
• Extensive hands-on experience with large language models and generative AI technologies.
• Strong knowledge of leading AI platforms, including OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock, and Google Vertex AI.
• Proven track record architecting and operating production-grade AI systems at scale.
• Experience with MLOps tooling such as MLflow, Kubeflow, Weights & Biases, or Neptune.ai.
• Deep familiarity with vector databases and retrieval systems, including Pinecone, Weaviate, Chroma, or FAISS.
• Strong grasp of cloud platforms and their AI/ML services, plus prompt engineering, retrieval-augmented generation, and fine-tuning techniques.
• Understanding of responsible AI principles, data privacy, and model security considerations.