Machine Learning Engineer

Job Description

We’re seeking a Machine Learning Engineer who thrives at the intersection of model development and infrastructure. You’ll be tasked with deploying and optimizing models that don’t just work in Jupyter notebooks, but scale reliably in production. Your contributions will directly impact critical systems like our real-time recommendation engine and demand forecasting pipelines.

This is not a research role. We care about operational ML — training on messy data, handling model drift, owning the retraining lifecycle, and deploying with observability. If you’ve ever spent more time debugging feature stores and pipeline DAGs than tuning hyperparameters, you’ll feel at home here.

Responsibilities

• Own ML deployment pipelines: Design, build, and maintain training and inference pipelines using tools like Airflow, Metaflow, or Vertex AI.
• Make models production-ready: Collaborate with data scientists to convert experimental notebooks into modular, tested, containerized services.
• Implement feedback loops: Enable systems to learn from user interaction, telemetry, or outcomes with minimal human intervention.
• Ensure model governance: Track lineage, monitor data drift, and automate retraining workflows with CI/CD integration.
• Performance tuning under constraints: Optimize inference latency, cost, and accuracy for models running on edge devices and batch clusters.
• Build internal tools: Develop CLI utilities or internal dashboards to surface model metrics to product and ops teams.

Requirements

• Strong Python and ML system design skills – You’re comfortable writing libraries, not just scripts.
• Deep experience with cloud-native ML tooling – GCP, AWS SageMaker, or Azure ML, ideally with Terraform or similar IaC tools.
• Expertise in model lifecycle management – Hands-on experience with tools like MLflow, TFX, Feast, or similar.
• Experience with real-world data – You’ve worked with noisy, incomplete datasets and know how to structure labeling or annotation pipelines.
• CI/CD mindset – Experience building ML CI pipelines using GitHub Actions, Argo, Jenkins, or similar.
• Monitoring experience – Familiarity with Prometheus, Grafana, or custom-built model observability systems.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

#GraphicDesignJobsOnline#WebDesignRemoteJobs #FreelanceGraphicDesigner #WorkFromHomeDesignJobs #OnlineWebDesignWork #RemoteDesignOpportunities #HireGraphicDesigners #DigitalDesignCareers#Dynamicbrandguru