Machine Learning Engineer (AWS/SageMaker/Dataiku/MLOps)

Machine Learning Engineer (AWS/SageMaker/Dataiku/MLOps)

*Visa sponsorship is NOT available, so NO visa candidates please – NO 3rd party vendors reachout – Direct W2 candidates ONLY*

Reading, PA OR Tampa, FL | Hybrid (2-3 days onsite per week) | Contract-to-Hire (or Direct Hire, client is flexible)

One onsite interview is required | Local candidates to work onsite & appear for onsite interview

Please send resume to: [email protected]

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.