Strategic AI Architect – Enterprise ML & MLOps

Highmark Health; Juneau (AK); On-site; USD 118,000 - 197,000; Full time

Job summary

enGen is seeking an AI Architect to define the end-to-end AI solution architecture, including data pipelines, model storage, inference engines, and front-end integration. You will select the optimal tech stack (LLMs, vector databases, orchestration) and lead MLOps best practices to ensure scalable, compliant AI systems across enterprise infrastructure.

You will work with Data Science, DevOps, and Product teams to align the AI roadmap with business needs, and you will mentor engineers while

Qualifications

• Architectural expertise across distributed systems and microservices.
• Design and govern end-to-end AI solution architectures.
• Experience with data pipelines and data schemas for structured and unstructured data.
• Ability to define scalable, secure AI ecosystems aligned to business goals.
• Knowledge of governance, privacy, and compliance requirements.

Responsibilities

• Define end-to-end AI solution architecture and tech stack.
• Lead cross-functional teams across data science, DevOps, and product.
• Establish best practices for MLOps and model deployment.
• Design scalable, low-latency AI systems and data architectures.
• Mentor engineers and present architectural decisions to leadership.

Skills

Architectural expertise

Distributed systems

Microservices architecture

API design

Cloud-native patterns

Data pipelines

MLOps best practices

Large language models (LLMs)

Retrieval-Augmented Generation (RAG)

Terraform

Ansible

AWS SageMaker

Azure ML

Vertex AI

Kubeflow

MLflow

Python/TensorFlow/PyTorch

Security and compliance

Lead cross-functional teams

Data privacy and GDPR/CCPA

Education

Bachelor’s Degree in Information Technology or closely related field

Master’s Degree

Tools

Terraform

Ansible

SageMaker

Azure ML

Vertex AI

Kubeflow

MLflow

BentoML

Job description

enGen is seeking an AI Architect to define the end-to-end AI solution architecture, including data pipelines, model storage, inference engines, and front-end integration. You will select the optimal tech stack (LLMs, vector databases, orchestration) and lead MLOps best practices to ensure scalable, compliant AI systems across enterprise infrastructure.

You will work with Data Science, DevOps, and Product teams to align the AI roadmap with business needs, and you will mentor engineers while