Senior MLOps Engineer

At AppRecode, we are passionate about building software that solves problems. We count on our DevOps Engineers to empower our users with a rich feature set, high availability, and stellar performance to pursue their missions.

As we expand our customer deployments, we seek an experienced Senior MLOps Engineer to build and scale machine learning systems. Specifically, we are searching for someone who will demonstrate a unique and informed viewpoint, enjoys collaborating with a cross-functional team, and help develop real-world solutions and positive user experiences at every interaction, with a strong track record of bringing products to life.

Join us, and you will have an opportunity to work with great Engineers, CEOs, CTOs, and other mature operators, a dynamic but still laid-back team (yes, you can combine that), agile development practices, the stack, and the approaches you choose to get the job done.

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.

Duration: Long-term engagement (full-time)

Start: December 1, 2025

Company

A company focused on machine learning systems at scale, requiring automation of ML workflows from data to production. They need infrastructure optimization for reliability, scalability, and efficiency of ML models in production environments.

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

Reporting & Collaboration

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

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