AI Engineer

The Opportunity

Do you crave the challenge of building AI systems that protect the world's most critical assets?

At Tenable, we're not just building cybersecurity solutions - we're revolutionising the way organisations defend themselves. We are at the forefront of leveraging massive datasets (Petabyte scale!) to develop cutting-edge AI models that empower our customers to stay ahead of the ever-evolving threat landscape.

We are looking for a Machine Learning Engineer to join our team. In this role, you will operate as a systems builder within the Data Science team. You will solve the "Last Mile" problem, ensuring that our experimental research doesn't just stay in a notebook but runs reliably as a production service.

Here's what you'll do:

• You will take models and wrap them in high-performance serving layers (e.g. Fast API). You will ensure these services are robust, testable, and ready for high-traffic environments.
• You will professionalise our AI lifecycle. You will implement standard patterns for model versioning, experiment tracking, and reproducibility, moving the team to a robust, standardised ML pipeline.
• You will leverage AWS services to build scalable inference architectures. You will work closely with our SRE and CICD teams to define the deployment patterns for Python-based AI services.
• You will design complex AI applications using modern AI tooling (e.g. LangGraph, AWS Bedrock, MCP Servers).
• You will own the container strategy for Data Science. You will build optimised Docker images and manage configuration to ensure our models run consistently from development to production.

We're looking for someone who:

• You write clean, production-grade Python. You understand the difference between a script that runs once and a service that runs forever.
• You are comfortable working with cloud-native architectures. You have experience with services like SageMaker, ECS, or Lambda and understand how to secure them.
• You are fluent in Docker and modern CI/CD tooling. You know how to debug a failing build and how to monitor a service in production.
• You have experience building applications on top of LLMs and you understand the ecosystem beyond the basics.

Qualifications:

• Bachelor’s or Master's degree in Computer Science, AI, or equivalent engineering experience.
• 3+ years of experience building Machine Learning or Generative AI applications in a professional environment.
• Strong proficiency in Python and frameworks like FastAPI.
• Deep experience with Docker and container orchestration.
• Proven experience with AWS services (SageMaker, Bedrock, etc.).
• Experience collaborating with DevOps/SRE teams to ship software.

Bonus Points:

• Experience with Model Interchange formats (ONNX) and optimising inference runtimes.
• Experience with Snowflake.
• Background in Cybersecurity or SaaS products.

Are you ready to join us in shaping the future of cybersecurity? Apply now!

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