Senior AI/MLOps Engineer

Upstaff LTD; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Upstaff LTD is seeking a Senior AI/MLOps Engineer to lead the development and operation of scalable ML infrastructure and production ML systems.

Focus areas include designing and improving ML pipelines, supporting real-time and batch inference, and ensuring reliable deployment. You will mentor engineers and establish best practices while collaborating with Data Science and engineering teams.

Qualifications

• Senior-level experience in AI/ML engineering, MLOps, or related field.
• Strong experience designing and operating production machine learning systems.
• Solid understanding of ML pipelines, model deployment, and production inference.
• Strong programming skills in Python and SQL.
• Experience with cloud infrastructure, preferably AWS.
• Experience with Kubernetes and containerized environments.
• Experience with MLflow and workflow orchestration tools such as Airflow or Dagster.
• Understanding of streaming and data-processing technologies such as Kafka.
• Strong communication and collaboration skills.
• Ability to provide technical leadership, mentor engineers, and drive engineering best practices.

Responsibilities

• Provide technical leadership and mentorship to Machine Learning Engineers.
• Design, architect, and oversee complex machine learning pipelines and production ML infrastructure.
• Develop and maintain systems supporting both real-time and batch inference.
• Collaborate with Data Science teams to deploy, monitor, and optimize machine learning models in production.
• Improve the performance, scalability, reliability, and maintainability of ML systems.
• Establish and promote best practices across software development, DevOps, and MLOps.
• Conduct code reviews and provide technical guidance to engineering teams.
• Evaluate and integrate new technologies and tools into the ML infrastructure.
• Work with technical and non-technical stakeholders to translate complex ML concepts into practical solutions.
• Contribute to the long-term technical direction of machine learning platforms and infrastructure.

Skills

Python

SQL

MLOps

Leadership

Mentoring

Code reviews

Tools

MLflow

Airflow

Dagster

Kafka

Kubernetes

AWS

Job description

• Senior AI/MLOps Engineer with extensive experience in designing, deploying, and maintaining scalable machine learning models and services in production.


• Expert in AWS cloud technologies, Kubernetes containerization, and orchestration.


• Skilled in Python and SQL, with strong skills in MLflow, Airflow/Dagster, Kafka, and stream processing.


• Demonstrated leadership in mentoring mid-level engineers, conducting code reviews, and driving strategic ML infrastructure improvements.


• Ability to collaborate with Data Science teams for model optimization and seamless deployment.


• Skilled in integrating cutting-edge ML tools and fostering best practices in MLOps and DevOps.

Role Overview

We are looking for a Senior AI/MLOps Engineer to provide technical leadership in the development and operation of scalable machine learning infrastructure and production ML systems.

The role focuses on designing and improving machine learning pipelines, supporting both real-time and batch inference, and ensuring reliable deployment and operation of models in production. The engineer will work closely with Data Science and engineering teams to optimize ML systems, introduce new technologies, and develop scalable long-term solutions.

The position also includes mentoring other engineers, conducting code reviews, establishing engineering best practices, and collaborating with both technical and non-technical stakeholders.

Responsibilities

• Provide technical leadership and mentorship to Machine Learning Engineers.


• Design, architect, and oversee complex machine learning pipelines and production ML infrastructure.


• Develop and maintain systems supporting both real-time and batch inference.


• Collaborate with Data Science teams to deploy, monitor, and optimize machine learning models in production.


• Improve the performance, scalability, reliability, and maintainability of ML systems.


• Establish and promote best practices across software development, DevOps, and MLOps.


• Conduct code reviews and provide technical guidance to engineering teams.


• Evaluate and integrate new technologies and tools into the ML infrastructure.


• Work with technical and non-technical stakeholders to translate complex ML concepts into practical solutions.


• Contribute to the long-term technical direction of machine learning platforms and infrastructure.

Technologies & Tools

• Cloud: AWS or other major cloud platforms


• Containers & Orchestration: Kubernetes


• MLOps: MLflow, model deployment, monitoring, and lifecycle management


• Workflow Orchestration: Airflow or Dagster


• Data & Streaming: Kafka and stream-processing technologies


• Programming: Python, SQL


• Engineering Practices: Software development, DevOps, MLOps, CI/CD, production system design


• Machine Learning: Model design, deployment, optimization, and maintenance at scale

Requirements

• Senior-level experience in AI/ML engineering, MLOps, or a related field.


• Strong experience designing and operating production machine learning systems.


• Solid understanding of ML pipelines, model deployment, and production inference.


• Strong programming skills in Python and SQL.


• Experience with cloud infrastructure, preferably AWS.


• Experience with Kubernetes and containerized environments.


• Experience with MLflow and workflow orchestration tools such as Airflow or Dagster.


• Understanding of streaming and data-processing technologies such as Kafka.


• Strong communication and collaboration skills.


• Ability to provide technical leadership, mentor engineers, and drive engineering best practices.