What are the responsibilities and job description for the Machine Learning Engineer (AWS/SageMaker/Dataiku0MLOps) position at InSource, Inc?
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.