Senior MLOps Architect for AI Platforms

Quantiphi, Inc.; United States; Hybrid; USD 180,000 - 230,000; Full time

Job summary

Quantiphi, Inc. is seeking an experienced ML/AI engineering professional to lead MLOps and LLMOps initiatives. You will architect pipelines, manage end-to-end model lifecycles, and drive production-grade ML platforms on AWS and Kubernetes.

Collaboration across data engineering and DevOps is essential, with a focus on governance, monitoring and security. The role emphasizes building scalable, observable systems and mentoring engineers to advance modern ML platform capabilities within a hybrid,

Qualifications

• 8+ years in ML/AI engineering or MLOps with strong architecture exposure.
• Strong expertise in AWS cloud-native ML stack (SageMaker, EKS, Lambda, API Gateway).
• Hands-on with at least one major MLOps toolset (MLflow, Kubeflow, SageMaker Pipelines, Airflow).
• Deep understanding of model lifecycle management and governance.
• Experience with LLMOps pipelines, prompt/version governance and evaluation metrics.
• Strong Python and cloud-native development patterns; security and IAM knowledge.

Responsibilities

• Architect and implement the MLOps strategy aligned with project proposals and delivery roadmap.
• Design and own enterprise-grade ML/LLM pipelines (training, validation, deployment, monitoring, CI/CD).
• Build container-oriented ML platforms (EKS-first) and evaluate orchestration tools.
• Lead hybrid MLOps + LLMOps workflows including governance and monitoring for LLM systems.
• Provide technical authority across projects; define standards for deployment, monitoring and governance.
• Mentor engineers on modern MLOps tools and best practices.

Skills

ML/AI engineering

MLOps

Python

Cloud-native

Tools

SageMaker

EKS

Lambda

API Gateway

CI/CD

Kubeflow

MLflow

Airflow

Seldon

KServe

Bedrock

Agentcore

Prometheus

Grafana

Terraform

Helm

Job description

Quantiphi, Inc. is seeking an experienced ML/AI engineering professional to lead MLOps and LLMOps initiatives. You will architect pipelines, manage end-to-end model lifecycles, and drive production-grade ML platforms on AWS and Kubernetes.

Collaboration across data engineering and DevOps is essential, with a focus on governance, monitoring and security. The role emphasizes building scalable, observable systems and mentoring engineers to advance modern ML platform capabilities within a hybrid,