- About Job/Role

Rapid7 is seeking a Principal AI Engineer to join our team as we expand and evolve our growing AI and MLOps efforts.
The role requires a strong foundation in applied AI R&D, software engineering, and MLOps/DevOps systems and tools.
Demonstrated track record of taking models created in AI R&D to production with repeatable deployment, monitoring, and observability patterns.
Combine expertise in AI/ML deployments, cloud systems, and software engineering to enhance product offerings and streamline platform functionalities.

- Roles and Responsibilities

Architect and manage the end-to-end design of ML production systems, including project scoping, data requirements, modeling strategies, and deployment.
Develop and maintain data pipelines, manage the data lifecycle, and ensure data quality and consistency throughout.
Assure robust implementation of ML guardrails and manage all aspects of service monitoring.
Develop and deploy accessible endpoints, including web applications and REST APIs, while maintaining data privacy and adherence to security best practices and regulations.
Share expertise and knowledge consistently with internal and external stakeholders, nurturing a collaborative environment and fostering the development of junior engineers.
Embrace agile development practices, valuing constant iteration, improvement, and effective problem-solving in complex and ambiguous scenarios.

- Required Qualification

15 years experience as a Software Engineer with 3-5 years focused on ML deployment (especially in AWS).
Solid technical experience in:
- Software engineering: developing APIs with Flask or FastAPI, strong Python knowledge.
- DevOps and MLOps: designing and integrating scalable AI/ML systems into production environments, CI/CD tooling, Docker, Kubernetes, cloud AI resource utilization and management.
- Pipelines, monitoring, and observability: data pre-processing, feature engineering, model monitoring and evaluation.
Strong written and verbal communication skills, able to communicate technical concepts clearly and create documentation of system architectures and implementation details.
Proven ability to collaborate across engineering, data science, product, and other teams to drive MLOps initiatives and ensure alignment on goals.
Established track record of mentoring and guiding junior engineers.
Growth mindset, welcoming complex problem-solving with a bias for action.

- Experience

- Skills

- Additional Information / Advantageous Skills

Understanding operational frameworks and limitations of AI and ML models.
Deploying resources to enable data scientists to fine-tune and experiment with LLMs.
Implementing model risk management strategies, including model registries, concept/covariate drift monitoring, and hyperparameter tuning.

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