Machine Learning Engineer (AWS/SageMaker/Dataiku0MLOps)

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

No visa candidates

Please send resume to: aghosh@copiastaffing.com

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.

Job description