Senior Machine Learning Engineer

What you'll do

• Architect and build a unified ML platform for model development, deployment, and monitoring.
• Design and implement scalable MLOps pipelines for training, deployment, and retraining.
• Manage and optimize AWS infrastructure to ensure cost-effectiveness, security, and availability for ML workloads.
• Build and maintain robust CI/CD pipelines for continuous integration and deployment of models.
• Develop monitoring and alerting systems using tools like Datadog to proactively resolve production issues.
• Lead architectural decisions and establish team standards for building and maintaining ML systems.
• Mentor junior engineers and share domain knowledge to build technical depth within the team.
• Ensure all ML pipelines and code adhere to security policies and handle sensitive data safely.

What we're looking for

• 5+ years of experience in machine learning engineering, MLOps, or data engineering.
• Proficiency in Python and experience with ML frameworks like scikit-learn, TensorFlow, or PyTorch.
• Strong cloud platform proficiency in AWS (preferred), GCP, or Azure for machine learning workloads.
• Experience with infrastructure-as-code tools such as Terraform or CloudFormation.
• Solid CI/CD experience using GitHub Actions or equivalent tools to operate deployment pipelines.
• Knowledge of event streaming platforms like Apache Kafka and monitoring tools like Datadog, Prometheus, or Grafana.
• Strong SQL skills and experience with data pipeline tooling including dbt, Glue, and Snowflake.
• Excellent communication skills for collaborating across data science, engineering, and product teams.