F2F Interview| C2H | ML Engineer (AWS/SageMaker/Dataiku/MLOps) | Reading, PA OR Tampa, FL

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

Additional Job Information:

Title : ML Engineer (AWS/SageMaker/Dataiku/MLOps)

Position Type : Right to Hire

Location : Reading, PA OR Tampa, FL

Description :

Interview: Video F2F

Reading, PA OR Tampa, FL | Hybrid (2-3 days onsite per week), Reading, PA is the manager's 1^st^ choice, Tampa, FL is 2^nd^.

Need local candidates within 1 hour of driving distance

Description :

Summary

We are seeking an experienced Machine Learning Engineer to build, deploy, and operationalize scalable, production-grade machine learning solutions. This is a hands-on engineering role focused on the complete ML lifecycle, from data preparation and model development through production deployment, monitoring, drift detection, and retraining.
The ideal candidate will bring strong hands-on experience with Python, AWS, Amazon SageMaker, Dataiku, and MLOps, with a track record of turning ML models into reliable enterprise production solutions.

Key Responsibilities

• Build end-to-end ML pipelines covering data preparation, feature engineering, training, validation, deployment, inference, monitoring, and retraining
• Develop, train, tune, and deploy ML models using Amazon SageMaker and Dataiku
• Operationalize models developed by Data Scientists and establish scalable MLOps practices
• Implement CI/CD, automated ML pipelines, model registries, versioning, deployment automation, and environment promotion
• Monitor model performance, data quality, feature/data/model drift, and inference health
• Build and support both batch and real-time inference solutions
• Optimize models for accuracy, scalability, latency, performance, and cost
• Troubleshoot production issues across data, feature, model, application, and infrastructure layers
• Build reusable ML components, APIs, libraries, and pipelines
• Partner closely with Data Scientists, Data Engineers, AI Engineers, cloud/platform teams, architects, and business stakeholders

Required Experience

• Strong hands-on Python 3.11 development experience
• Deep hands-on Amazon SageMaker experience across model development, training, hyperparameter tuning, deployment, inference, monitoring, and lifecycle management
• Strong hands-on Dataiku experience for data preparation, feature engineering, ML development, and operational workflows
• Strong AWS experience supporting production ML workloads and cloud-native architectures
• Strong end-to-end MLOps and production ML lifecycle experience
• Experience with model registries, automated ML pipelines, CI/CD, versioning, monitoring, drift detection, and retraining
• Experience building batch and real-time ML inference pipelines
• Experience with REST APIs, Git, automated testing, Docker/containerization, and CI/CD
• Understanding of AWS security including IAM, secrets management, encryption, authentication/authorization, and least-privilege access
• Strong knowledge of ML techniques including classification, regression, clustering, forecasting, anomaly detection, and recommendation systems

Nice To Have

• Experience with SageMaker Pipelines, Model Registry, Feature Store, Model Monitor, SageMaker Unified Studio, Dataiku Automation, Kubernetes/EKS, model governance, responsible AI, Amazon Bedrock, RAG, or Generative AI is a plus.
• Relevant AWS certifications are also a plus.