Job description

As a Senior MLOps Engineer, you will have a high level of ownership over the platform the machine learning team builds on, spanning model training, experimentation, deployment, monitoring, and the data infrastructure supporting production ML. You must have firsthand experience building MLOps infrastructure from the ground up or taking an existing platform through significant scaling and continuing to operate it as it matured. Responsibilities include: • Own and extend the ML platform end to end, including training orchestration, experiment tracking, model registry, deployment, and production monitoring. • Build and operate data pipelines supporting model training and online inference. • Build backend services and APIs that support the deployment and operation of ML models in production. • Design processes for backfills, replays, and recovery when upstream data feeds fail. • Ensure training data accurately reflects what was known at the point in time it represents. • Build and manage reliable processes for moving trained models into production. • Ensure model training runs and results are reproducible. • Monitor deployed model performance over time, including models where outcomes are confirmed later. • Manage training and inference costs as data volume and the number of production models grow. • Contribute to technical architecture design and reviews. • 5-7+ years of professional software engineering or data engineering experience. • 2+ years building and operating machine learning systems in production. • Hands on experience building MLOps infrastructure from the ground up or significantly scaling an existing ML platform. • Experience continuing to own and operate ML infrastructure as it matured. • Experience supporting multiple production models, including deployment, serving, monitoring, and scaling. • Strong backend engineering experience, including building production systems, services, or APIs in Python. • Experience building and maintaining production data pipelines at scale, including managing their operating costs. • Experience working with multiple external data sources that behave differently and may arrive inconsistently. • Working knowledge of machine learning concepts with enough depth to effectively collaborate with ML engineers. • Entrepreneurial mindset and interest in working within a fast moving startup environment. Preferred Experience • Advanced proficiency with Python 3 in mature production environments + Kubernetes + PostgreSQL. • Experiment tracking, model registry, and model serving frameworks. • Workflow orchestration. • Infrastructure as code tooling. • Geospatial or time series systems. • Large real time data flows. • Experience working for a data intelligence company. • Bachelor's degree in Computer Science or a related field. About SaaS Talent SaaS Talent is more than just a recruiting company. We're your hiring, business development and growth partner with 20+ years of experience in SaaS and Hi-Tech that helps you scale and transform your business. We've worked with 100+ companies and helped them achieve their goals. From streamlining sales, marketing, and operations to hiring ideal talent and getting funding, if you're struggling to grow, we're an ideal choice. Reach out to us at www.saas-talent.com to learn more about how we can help you . SMS Communication Consent Disclaimer By applying for this position, you agree to receive text message updates from SaaS Talent related to job opportunities. Standard message and data rates may apply, and messaging frequency varies. Text HELP for help and STOP to cancel. Learn more about our opt-in SMS Communication consent policy here: saas-talent.com

Requirements

Must-haves

You must have firsthand experience building MLOps infrastructure from the ground up or taking an existing platform through significant scaling and continuing to operate it as it matured.
Own and extend the ML platform end to end, including training orchestration, experiment tracking, model registry, deployment, and production monitoring.
Build and operate data pipelines supporting model training and online inference.
Build backend services and APIs that support the deployment and operation of ML models in production.
Design processes for backfills, replays, and recovery when upstream data feeds fail.
Ensure training data accurately reflects what was known at the point in time it represents.
Build and manage reliable processes for moving trained models into production.
Ensure model training runs and results are reproducible.
Monitor deployed model performance over time, including models where outcomes are confirmed later.
Manage training and inference costs as data volume and the number of production models grow.
Contribute to technical architecture design and reviews.
5-7+ years of professional software engineering or data engineering experience.
2+ years building and operating machine learning systems in production.
Hands on experience building MLOps infrastructure from the ground up or significantly scaling an existing ML platform.
Experience continuing to own and operate ML infrastructure as it matured.
Experience supporting multiple production models, including deployment, serving, monitoring, and scaling.

Nice-to-haves

Advanced proficiency with Python 3 in mature production environments + Kubernetes + PostgreSQL.
Experiment tracking, model registry, and model serving frameworks.
Workflow orchestration.
Infrastructure as code tooling.
Geospatial or time series systems.
Large real time data flows.
Experience working for a data intelligence company.
Bachelor's degree in Computer Science or a related field.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlpythonkubernetes

Platform: smartrecruiters
Effort: medium