Senior Lead ML Engineer: Scale Production Models in Cloud

Jobtailor; Illinois; On-site; USD 150,000 - 190,000; Full time

Job summary

Jobtailor is seeking a senior ML Engineer to design, build, and deploy data-intensive ML solutions in a cloud environment. You will collaborate with product and data science teams to deliver scalable ML components and production-grade pipelines.

The role requires guiding infrastructure decisions, optimizing models, and leading cross-functional Agile teams while ensuring responsible AI practices and robust monitoring.

Qualifications

• Bachelor's Degree required; advanced degree preferred.
• 8+ years designing and building data-intensive solutions with distributed computing.
• 4+ years programming in Python, Scala, or Java.
• 3+ years leading teams developing ML solutions.
• Experience deploying ML in public clouds (AWS/Azure/GCP).
• 4+ years with ML frameworks (scikit-learn, PyTorch, TensorFlow).

Responsibilities

• Design, build, and deliver ML models and components solving real-world business problems.
• Collaborate with Product and Data Science teams.
• Make ML infrastructure decisions including model choice, data/feature selection, training, tuning, and validation.
• Write and test application code, develop and validate ML models, and automate tests and deployment.
• Collaborate on a cross-functional Agile team developing big data and ML applications.
• Retrain, maintain, and monitor production models.

Skills

Machine Learning

Python

Scala

Java

Distributed Computing

Data Pipelines

CI/CD

Cloud Platforms

Team Leadership

ML Frameworks

Education

Bachelor's Degree

Master's or Doctoral Degree (Preferred)

Tools

scikit-learn

PyTorch

TensorFlow

Spark

Dask

Job description

Jobtailor is seeking a senior ML Engineer to design, build, and deploy data-intensive ML solutions in a cloud environment. You will collaborate with product and data science teams to deliver scalable ML components and production-grade pipelines.

The role requires guiding infrastructure decisions, optimizing models, and leading cross-functional Agile teams while ensuring responsible AI practices and robust monitoring.