MLOps Engineer - MoneyLion

Skills

About the Role

You will help design, build, and operate the next generation of the machine learning platform and infrastructure, enabling data scientists and ML engineers to reliably take models from experimentation to production at scale. You will work closely with Data Scientists, Data Engineers, and AI/ML Engineering teams to streamline end-to-end ML workflows and improve system design and architecture for production-grade ML solutions.

Requirements

• Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or related field, or equivalent practical experience
• Solid programming skills in Python (preferred) or similar languages, with experience building production-grade services and tools for data/ML workflows
• Hands-on experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization/orchestration technologies such as Docker and Kubernetes
• Experience implementing CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Jenkins, Azure DevOps) for data or ML projects
• Familiarity with ML frameworks and tooling (e.g., scikit-learn, TensorFlow, PyTorch, MLflow, Kubeflow, SageMaker, Vertex AI, or equivalents)
• Strong understanding of software engineering best practices: code reviews, testing, logging, monitoring, and documentation
• Good collaboration and communication skills, with experience working in cross-functional teams (Data Science, Data Engineering, Product, and Operations)
• Preferred experience in a dedicated MLOps / ML Platform / ML Infrastructure role across multiple model lifecycles (from prototype to large-scale production)
• Preferred experience building or supporting feature stores, model registries, and experiment tracking systems
• Preferred exposure to streaming and near-real-time data processing (e.g., Kafka, Kinesis, Pub/Sub)
• Knowledge of data governance, privacy, and security considerations in ML systems
• Preferred experience with observability stacks (e.g., Prometheus, Grafana, ELK/EFK, Datadog) and setting up model and data quality monitors
• Familiarity with LLM / GenAI workloads and associated tooling is a plus

Responsibilities

• Design, build, and maintain ML infrastructure and tooling to support the full ML lifecycle (data preparation, training, evaluation, deployment, monitoring, and retraining)
• Develop and maintain CI/CD pipelines for ML models, including automated testing, validation, and safe rollout/rollback strategies
• Implement robust model deployment patterns (batch, real-time, streaming) and ensure scalability, reliability, and low-latency performance in production environments
• Build and operate monitoring and observability for ML systems (data drift, model performance, system health), and define alerting/incident response processes
• Partner with Data Scientists and ML Engineers to productize models, including feature engineering pipelines, model packaging, and environment standardization
• Collaborate with Data Engineering to integrate ML workloads into data platforms and pipelines, ensuring data quality, lineage, and governance
• Drive best practices in MLOps, including versioning (data, model, code), experiment tracking, reproducibility, and documentation
• Contribute to security, compliance, and cost optimization aspects of ML infrastructure (access control, secrets management, resource utilization)
• Provide technical guidance and support to cross-functional teams on ML platform usage, tools, and workflows

Benefits

• Flexible working options
• Time off
• Competitive pay
• Well-being programs