Apprecode

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.