Senior MLOps Engineer

Summary

Join Massive Rocket, a rapidly growing Martech agency specializing in Braze and Snowflake, as a Senior MLOps Engineer. You will play a crucial role in designing, implementing, and optimizing robust, scalable, and efficient machine learning infrastructure. Collaborate with data scientists, ML engineers, and product teams to streamline model deployment, monitoring, and operationalization, ensuring reliable and scalable AI-driven solutions. This role offers a fast-paced, supportive, and remote-first work environment with opportunities for growth and collaboration with a global team.

Requirements

• 5+ years of experience in MLOps, DevOps, or Data Engineering with a strong focus on ML model deployment
• Strong expertise in ML lifecycle automation, model versioning, and monitoring
• Hands-on experience with ML pipeline orchestration tools such as Apache Airflow, Kubeflow, or MLflow
• Proficiency in programming languages such as Python (Pandas, NumPy, TensorFlow/PyTorch) & SQL
• Experience with containerization (Docker, Kubernetes) and CI/CD pipelines for ML
• Familiarity with cloud platforms (Azure preferred, AWS or GCP also considered)
• Experience with infrastructure as code (Terraform, CloudFormation)
• Strong understanding of data engineering concepts and working with large-scale data processing frameworks (Spark, Databricks)
• Familiarity with version control systems (e.g., Git) and collaborative development workflows
• Excellent problem-solving skills and ability to work in a fast-paced environment
• Strong communication skills (English C1 level) and experience working in client-facing roles

Responsibilities

• Automate and streamline the deployment of ML models to production environments
• Implement CI/CD pipelines for ML models, ensuring efficient retraining and deployment
• Optimize model inference performance and scalability
• Design and manage scalable cloud-based ML infrastructure (Azure, AWS, or GCP)
• Utilize Kubernetes and Docker for containerized ML model deployment
• Optimize cloud costs while maintaining high availability and performance
• Build and maintain scalable ETL/ELT data pipelines for ML model training
• Automate data ingestion, transformation, and feature engineering processes
• Leverage Apache Airflow or similar tools to schedule and monitor data workflows
• Implement monitoring solutions to track model performance, drift, and anomalies
• Automate logging, alerting, and retraining mechanisms to improve model robustness
• Ensure compliance with security and governance standards
• Use Terraform or similar tools to automate the provisioning of cloud infrastructure
• Define reusable, modular configurations to support scalable deployment
• Work closely with data scientists, software engineers, and DevOps teams to improve workflows
• Maintain comprehensive documentation of ML infrastructure, processes, and best practices

Preferred Qualifications

• Experience with real-time model serving using tools like TensorFlow Serving, TorchServe, or Triton Inference Server
• Hands-on experience with feature stores like Feast or Tecton
• Experience working with compliance-driven industries (e.g., finance, healthcare, regulated environments)
• Knowledge of graph databases, NLP models, or reinforcement learning workflows
• Demonstrated experience with Terraform for infrastructure provisioning and management

Benefits

• Fast-moving environment – you will never stop learning and growing
• Supportive and positive work culture with an emphasis on our values
• International presence – work with team members in Europe, the US, and around the globe
• 100% remote forever
• Organised team events and outings