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

InSource, Inc; Reading (Berks County); Hybrid; USD 120,000 - 180,000; Full time

Job summary

InSource, Inc. is seeking an experienced Machine Learning Engineer to build, deploy, and operationalize production-grade ML solutions. You will own the full ML lifecycle from data prep to retraining, with emphasis on Python, AWS, SageMaker, Dataiku, and MLOps.

The role demands strong hands-on experience with end-to-end ML tasks, CI/CD, and production-scale pipelines in a hybrid environment, with onsite interviews for local candidates in Reading, PA or Tampa, FL.

Qualifications

• Deep hands-on experience with Amazon SageMaker covering model development, training, deployment, and monitoring.
• Strong Dataiku experience for data prep, feature engineering, ML development, and production workflows.
• Solid AWS production ML experience and cloud-native architectures.
• End-to-end MLOps and production ML lifecycle experience including registries, automated pipelines, and drift detection.

Responsibilities

• Build end-to-end ML pipelines for data prep, feature engineering, training, validation, deployment, inference, monitoring, and retraining.
• Develop, train, tune, and deploy ML models using SageMaker and Dataiku.
• Operationalize models and establish scalable MLOps practices.
• Implement CI/CD, automated ML pipelines, model registries, versioning, deployment automation, and environment promotion.
• Monitor model performance, data quality, drift, and inference health.
• Support both batch and real-time inference solutions.
• Optimize models for accuracy, scalability, latency, and cost.
• Troubleshoot production issues across data, features, models, apps, and infra.
• Build reusable ML components, APIs, libraries, and pipelines.
• Collaborate with Data Scientists, Data Engineers, cloud/platform teams, architects, and stakeholders.

Skills

SageMaker

Dataiku

AWS

MLOps

CI/CD

Docker

REST APIs

Tools

Git

Docker

Kubernetes

CI/CD tooling

Job description

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

No current or future employment sponsorship available.

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

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

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

• 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.