Senior MLOps Engineer — Production AI on Azure

Fisher Investments; Plano (TX); On-site; USD 170,000 - 210,000; Full time

Benefits offered by this job

100% paid medical, dental and vision

401(k) match

Paid time off and holidays

Family Support programs

Hybrid work program

Job summary

Fisher Investments is seeking a Senior MLOps Engineer to build and operate end-to-end ML pipelines on Azure and Databricks. The role focuses on turning experimental workflows into scalable, production-ready services with secure CI/CD for model artifacts and AI capabilities across enterprise applications.

You will deploy and scale model-serving endpoints, manage infrastructure as code, and collaborate with AI leads and platform teams to ensure reliability, performance, and governance in

Qualifications

• 10+ years of experience in data-driven software solutions with 5+ years in ML engineering.
• 8+ years Python development including production services, APIs, or data pipelines.
• Proven experience deploying ML/generative AI in production.
• Experience with Databricks (Delta Lake, Auto Loader, Workflows, Feature Engineering).
• CI/CD for data and ML systems.

Responsibilities

• Design, build, and operate end-to-end data and ML pipelines on Azure and Databricks.
• Develop production-grade APIs and microservices with Python/FastAPI.
• Operate and scale model-serving endpoints and containerized inference services.
• Establish versioning, experiment tracking, and deployment traceability.
• Collaborate with AI leads and platform teams to productionize experiments.

Skills

Python development

Production-grade ML systems

Data pipelines

CI/CD for ML workflows

Experiment tracking and reproducibleML

Education

Bachelor's degree in CS/AI or related field

Tools

Databricks

Azure

Docker

Kubernetes

FastAPI

Job description

Fisher Investments is seeking a Senior MLOps Engineer to build and operate end-to-end ML pipelines on Azure and Databricks. The role focuses on turning experimental workflows into scalable, production-ready services with secure CI/CD for model artifacts and AI capabilities across enterprise applications.

You will deploy and scale model-serving endpoints, manage infrastructure as code, and collaborate with AI leads and platform teams to ensure reliability, performance, and governance in