ML Engineer: End-to-End MLOps (AWS/SageMaker, Dataiku)

Shift Code Analytics; Reading (Berks County); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Shift Code Analytics seeks an experienced Machine Learning Engineer to build, deploy, and operate scalable ML solutions across the full lifecycle, from data prep to retraining.

The ideal candidate has strong Python, AWS SageMaker, Dataiku expertise and hands-on MLOps experience, plus a track record turning models into production-ready services. This role is hybrid with 2-3 days onsite in Reading, PA or Tampa, FL.

Qualifications

• Strong hands-on Python development (3.11)
• Deep SageMaker experience: development, training, tuning, deployment, monitoring
• Dataiku experience for data prep and ML workflows
• Cloud experience with production ML workloads on AWS
• End-to-end MLOps and production ML lifecycle knowledge
• Experience with model registries, CI/CD, versioning, monitoring, drift detection
• Experience building batch and real-time ML inference pipelines
• Familiarity with REST APIs, Git, testing, and containerization

Responsibilities

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

Skills

Python

SageMaker

Dataiku

MLOps

CI/CD

REST APIs

Docker

Git

IAM

Hyperparameter Tuning

Tools

SageMaker

Dataiku

Docker

Kubernetes

Job description

Shift Code Analytics seeks an experienced Machine Learning Engineer to build, deploy, and operate scalable ML solutions across the full lifecycle, from data prep to retraining.

The ideal candidate has strong Python, AWS SageMaker, Dataiku expertise and hands-on MLOps experience, plus a track record turning models into production-ready services. This role is hybrid with 2-3 days onsite in Reading, PA or Tampa, FL.