ML Engineer: AWS SageMaker, Dataiku & MLOps

InSource, Inc; Reading (Berks County); Hybrid; USD 120,000 - 180,000; Full time

Job summary

InSource, Inc. is seeking an experienced Machine Learning Engineer to build, deploy, and operationalize production-grade ML solutions. You will own the full ML lifecycle from data prep to retraining, with emphasis on Python, AWS, SageMaker, Dataiku, and MLOps.

The role demands strong hands-on experience with end-to-end ML tasks, CI/CD, and production-scale pipelines in a hybrid environment, with onsite interviews for local candidates in Reading, PA or Tampa, FL.

Qualifications

• Deep hands-on experience with Amazon SageMaker covering model development, training, deployment, and monitoring.
• Strong Dataiku experience for data prep, feature engineering, ML development, and production workflows.
• Solid AWS production ML experience and cloud-native architectures.
• End-to-end MLOps and production ML lifecycle experience including registries, automated pipelines, and drift detection.

Responsibilities

• Build end-to-end ML pipelines for data prep, feature engineering, training, validation, deployment, inference, monitoring, and retraining.
• Develop, train, tune, and deploy ML models using SageMaker and Dataiku.
• Operationalize models and establish scalable MLOps practices.
• Implement CI/CD, automated ML pipelines, model registries, versioning, deployment automation, and environment promotion.
• Monitor model performance, data quality, drift, and inference health.
• Support both batch and real-time inference solutions.
• Optimize models for accuracy, scalability, latency, and cost.
• Troubleshoot production issues across data, features, models, apps, and infra.
• Build reusable ML components, APIs, libraries, and pipelines.
• Collaborate with Data Scientists, Data Engineers, cloud/platform teams, architects, and stakeholders.

Skills

SageMaker

Dataiku

AWS

MLOps

CI/CD

Docker

REST APIs

Tools

Git

Docker

Kubernetes

CI/CD tooling

Job description

InSource, Inc. is seeking an experienced Machine Learning Engineer to build, deploy, and operationalize production-grade ML solutions. You will own the full ML lifecycle from data prep to retraining, with emphasis on Python, AWS, SageMaker, Dataiku, and MLOps.

The role demands strong hands-on experience with end-to-end ML tasks, CI/CD, and production-scale pipelines in a hybrid environment, with onsite interviews for local candidates in Reading, PA or Tampa, FL.