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# Machine Learning (ML) Engineer

- **Company:** KBC Technologies Inc.
- **Location:** New York, NY, United States
- **Experience:** Experienced
- **Contract:** Contract
- **Skills:** Automation of Tests, Computer Programming, Continuous Integration, Information Engineering, Information Leak Prevention, DevOps, Distributed Computing Environment, Monitoring of Systems, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Azure Machine Learning, Software Engineering, Systems Integration, Feature Store, Graphics Processing Unit (GPU), Pytorch, Containerization, Kubernetes, Low Latency, Deployment Automation, Performance Monitor, Machine Learning Operations, Drift Detection, Model Registry, Restful APIs, Software Version Control, Docker
- **Published:** October 18, 2026
- **Apply:** https://www.dice.com/job-detail/1ee31c36-4d17-4c37-a770-97d37ec4e4ef

## About the Role

We are looking for an experienced Machine Learning (ML) Engineer to design, develop, deploy, and maintain production-grade machine learning solutions. The ideal candidate will have strong Python programming skills, hands-on experience with TensorFlow or PyTorch, and a proven track record of deploying ML models into production environments., * 5+ years of software engineering experience, including 2+ years deploying and maintaining machine learning models in production.

 * Strong programming skills in Python.
 * Hands-on experience with machine learning frameworks such as TensorFlow and/or PyTorch.
 * Experience developing and integrating REST APIs for ML services.
 * Practical experience with Docker and Kubernetes for containerization and orchestration.
 * Strong understanding of ML system design, including data leakage, training-serving skew, model drift, and performance monitoring.
 * Experience implementing MLOps practices, including CI/CD, automated testing, model versioning, and deployment automation.
 * Understanding of online and batch inference architectures and scalable model-serving patterns., * Experience with feature stores, model registries, and model-monitoring platforms.
 * Knowledge of GPU optimization and distributed model training.
 * Experience with responsible AI tools, model governance, and compliance requirements.
 * Familiarity with production ML observability, performance optimization, and automated model lifecycle management.
 * Experience working collaboratively across Data Science, Data Engineering, and DevOps teams.

Key Technical Skills

Must-Have Skills: Python, TensorFlow and/or PyTorch, Docker, REST APIs, Kubernetes, MLOps, Production ML Deployment, CI/CD.

Good-to-Have Skills: Feature Stores, Model Registry, Model Monitoring, GPU Optimization, Distributed Training, Responsible AI, Model Drift Detection., The ideal candidate is a hands-on ML Engineer with strong software engineering fundamentals and experience taking machine learning models from experimentation to production. The candidate should be comfortable building scalable ML pipelines, automating model deployment, monitoring production performance, and troubleshooting issues across the ML lifecycle.

## Description

The candidate will work closely with Data Science, Data Engineering, and DevOps teams to transform data science prototypes into scalable, reliable, and maintainable ML services and pipelines. Experience with MLOps, containerization, model monitoring, and production ML system design is essential., * Translate data science prototypes into production-ready machine learning services and pipelines.

 * Develop training and inference code with reproducibility, version control, and automated testing.
 * Build and maintain scalable model-serving solutions for online and offline inference.
 * Optimize model performance, batching, latency, throughput, and resource utilization.
 * Implement and manage ML lifecycle tools, including experiment tracking, model registries, deployment automation, and monitoring.
 * Collaborate with Data Engineering teams to develop feature pipelines and establish data contracts.
 * Implement model monitoring, drift detection, and performance regression checks.
 * Establish rollback strategies, production health checks, and incident response procedures for ML services.
 * Apply CI/CD and DevOps practices to automate ML model testing, deployment, and maintenance.
 * Ensure ML systems are reliable, scalable, secure, and maintainable in production environments.