> Markdown version of [/jobs/ext/3604483-ml-engineer](https://www.wearedevelopers.com/jobs/ext/3604483-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# ML Engineer

- **Company:** Shiftcode Analytics, Inc
- **Location:** Reading, PA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, User Authentication, Automation of Tests, Cloud Engineering, Cluster Analysis, Continuous Integration, Data Cleansing, Identity and Access Management, Python (Programming Language), Key Management, Machine Learning, Recommender Systems, Software Deployment, Management of Software Versions, Feature Store, Feature Engineering, Retrieval-Augmented Generation, Generative AI, Git, Containerization, Kubernetes, Amazon Bedrock, Deployment Automation, Machine Learning Operations, Drift Detection, Dataiku, Model Registry, Restful APIs, Docker
- **Published:** October 7, 2026
- **Apply:** https://www.dice.com/job-detail/a7b21e81-ad30-40d9-a973-352d65f68aa2

## About the Role

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., * 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.

## Description

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., 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., * 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