Senior MLOps Engineer

AppRecode, Inc.; Town of Middletown (NY); On-site; USD 120,000 - 160,000; Full time

Job summary

AppRecode, Inc. is looking for a Senior MLOps Engineer to enhance and scale their machine learning systems. This full-time remote position involves building end-to-end ML pipelines, automating model training and deployment, and ensuring the reliability of production ML operations. The ideal candidate will have extensive experience in MLOps practices and strong knowledge of cloud platforms such as AWS, GCP, or Azure, along with skills in Docker and Kubernetes. Flexible work hours will support team collaboration.

Qualifications

• Experience in automating machine learning model training and deployment processes.
• Strong knowledge of cloud platforms (AWS / GCP / Azure).
• Proficient in Docker and Kubernetes for managing containerized applications.

Responsibilities

• Automate machine learning model training and deployment processes using CI/CD pipelines.
• Build end-to-end MLOps pipelines in cloud platforms.
• Implement monitoring for ML models in production environments.

Skills

MLOps practices

CI/CD for ML

Docker

Kubernetes

Cloud platforms

Python

Infrastructure as Code

ML frameworks

Model deployment

Job description

About the Position

The client is focused on improving and scaling machine learning systems. They need a senior MLOps engineer to build end‑to‑end ML pipelines in the cloud, automate model training and deployment, and ensure production ML systems are monitored, reliable, and scalable.

Start: December 1, 2025

Key Responsibilities

• Automate machine learning model training and deployment processes using CI/CD pipelines
• Build end-to-end MLOps pipelines in cloud platforms (AWS / GCP / Azure)
• Implement monitoring and observability for ML models in production environments
• Optimize infrastructure for ML workloads to improve reliability, scalability, and efficiency
• Deploy and manage containerized ML applications using Docker and Kubernetes
• Implement model versioning, experiment tracking, and model registry solutions
• Set up data pipelines and feature stores for ML model training
• Ensure ML model performance monitoring, drift detection, and retraining automation
• Collaborate with data scientists to operationalize ML models from development to production
• Implement infrastructure as code for ML infrastructure using Terraform or similar tools

Reports to: Client’s Engineering Manager / CTO

Collaborates with: Data Science team, Engineering teams, DevOps team

Technologies

Must-have: MLOps practices, CI/CD for ML (GitHub Actions, GitLab CI, Azure DevOps), Docker, Kubernetes, Cloud platforms (AWS / GCP / Azure), Python, Infrastructure as Code (Terraform), ML frameworks (TensorFlow, PyTorch, scikit-learn), Model deployment (SageMaker, Vertex AI, Azure ML, or Kubeflow)

Nice-to-have: MLflow, Weights & Biases, DVC, Feature stores (Feast, Tecton), Model monitoring (Evidently, WhyLabs), Apache Airflow, Spark, Ray, Helm, ArgoCD, Prometheus, Grafana, Data versioning, A/B testing for models

Soft Skills

• Fluent English (conversational and written)
• Strong problem-solving and analytical skills
• Ability to work independently and implement ML processes end-to-end
• Collaboration skills working with data scientists and engineers
• Understanding of ML model lifecycle from data to production
• Highly self‑managed and able to plan, estimate, and execute tasks

Challenges & Milestones

First 90 Days: Assess current ML infrastructure, implement initial MLOps automation, set up model monitoring for production models

Months 3-6: Build end-to-end ML pipelines with automated training and deployment, implement experiment tracking and model registry, optimize infrastructure costs

Months 6-12: Full MLOps platform operational with automated retraining, drift detection, A/B testing capabilities, and scalable infrastructure supporting multiple ML models

Working Hours

Full-time (40 hours/week), Remote

Flexible hours with reasonable overlap for team collaboration

We are seeking a Senior MLOps Engineer to build and scale machine learning systems in the cloud. This role focuses on automating ML model training, deployment, and monitoring to ensure reliable production ML operations.