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

Hi,
I hope you are doing well.
Please let me know if you are looking for a job change and interested in the below position.

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.

Thanks & Regards,

Anikat Kumar

Sr. Technical Recruiter

ShiftCode Analytics Inc.

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