Senior MLOps Architect — Scalable AI Platform & Cloud

HCA-Healthcare; Nashville (TN); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Comprehensive benefits

Wellbeing support

Time away from work programs

401(k) match

Tuition assistance

Job summary

HCA Healthcare is seeking a Staff MLOps Engineer to shape and scale our AI/ML platform within ITG, partnering with data scientists, product managers, and cloud engineers. The role emphasizes reliability, responsible AI, and cross-functional collaboration to deliver scalable ML solutions.

You will design robust DevOps pipelines for ML workloads, leverage cloud expertise (GCP) and IaC, and mentor teams while aligning platform efforts with business objectives.

Qualifications

• Bachelor's degree preferred
• Master's degree in Computer Science, Data Science, AI, or related field preferred
• 5+ years of experience in ML Ops, DevOps, or related role required

Responsibilities

• Platform Development: Lead the enhancement of the MLOps platform for developers and ML engineers.
• Cloud Infrastructure Design and Management: Architect and manage cloud infrastructure using IaC tools (Terraform).
• Cross-Functional Collaboration: Partner with data science, product managers, and cloud engineers to align platform with business goals.
• AI/ML Reliability and Observability: Ensure platform reliability, observability, and fault tolerance.
• DevOps for ML Workloads: Build and maintain robust ML pipelines for training, testing, deployment, and monitoring.

Skills

Python

Cloud architecture

AI/ML concepts

Leadership

Communication

Education

Bachelor's degree

Master's degree

Tools

Google Cloud Platform

Terraform

Job description

HCA Healthcare is seeking a Staff MLOps Engineer to shape and scale our AI/ML platform within ITG, partnering with data scientists, product managers, and cloud engineers. The role emphasizes reliability, responsible AI, and cross-functional collaboration to deliver scalable ML solutions.

You will design robust DevOps pipelines for ML workloads, leverage cloud expertise (GCP) and IaC, and mentor teams while aligning platform efforts with business objectives.