Staff AI Engineer / Lead AI Engineer

About the role

The AI Center of Excellence team includes Data Scientists and AI Engineers that work together to conduct research, build prototypes, design features and build production AI components and systems. Our mission is to leverage the best available technology to protect our customers' attack surfaces. We partner closely with Detection and Response teams, including our MDR service, to leverage AI/ML for enhanced customer security and threat detection. We operate with a creative, iterative approach, building on 20+ years of threat analysis and a growing patent portfolio. We foster a collaborative environment, sharing knowledge, developing internal learning, and encouraging research publication. If you're passionate about AI and want to make a major impact in a fast-paced, innovative environment, this is your opportunity. The technologies we use include: - Python is used for analysis and modelling, with numpy, pandas, and scientific computing libraries. - Jupyter notebooks are used for local and remote analysis. - scikit-learn is used for machine learning. - Anomaly detection is used for large-scale unlabeled data analysis. - LLM/GenAI toolchains, including HuggingFace, Transformers, LangChain, CrewAI, and Agentic architectures, are used. - The AWS cloud ecosystem, including SageMaker, Bedrock, Lambda, EKS, and S3, is used. - Agentic AI platforms, including multi-agent orchestration, tool use, reasoning frameworks, and LLMOps, are used. - EKS is used for application deployment. - Terraform is used for infrastructure as code. Rapid7 is seeking a Staff AI Engineer to join our Data Science team as we expand and evolve our growing AI and MLOps efforts. You should have a strong foundation in software engineering and applied R&D in one of the key areas we focus on - traditional machine learning, neural networks, or generative AI. In this intersectional role, you will combine your expertise in AI/ML deployments, cloud systems and software engineering to enhance our product offerings and streamline our platform's functionalities. This role is ideal for someone who is: - Strong background in data science. - Proficient with AWS and ML/LLM infrastructure. - Excited about agentic AI, autonomous workflows, tool-augmented LLM systems, and building the future of AI-driven security. In this role, you will: - Work with security teams to define, scope, and design research efforts for new threat detections, automations, and AI-driven workflows. - Collaborate with data scientists to transform research into production-ready solutions, mentoring on both methods and execution. - Research, build, and evaluate ML and generative/LLM models, including agentic and multi-step reasoning systems. - Design and optimise agentic architectures, including tool-calling, decision-making, memory, orchestration, and evaluation frameworks. - Partner with engineering teams to ship AI features, integrating models into high-scale systems. - Deploy AI/ML workloads in AWS using SageMaker, Bedrock, Lambda, EKS, Step Functions, and related services. - Contribute to our LLMOps and MLOps workflows, improving evaluation pipelines, observability, reproducibility, and governance. - Support development of AI security features, improving reasoning, context understanding, automation, and analyst workflows. - Embrace agile development, iterative experimentation, and collaborative problem solving. - Mentor junior team members and uplift the technical bar for agentic AI and ML engineering. The skills you'll bring include: Core - 8-12 years of experience as a Data Scientist, ML Engineer, or AI Engineer. - Strong end-to-end practical expertise in ML/AI/DS. - Able to explore, experiment, and deliver independently. Proficiency in: - scikit-learn is used for classical machine learning. - PyTorch, TensorFlow, and Keras are used for deep learning. - HuggingFace, LangChain, and Transformers are especially useful for LLMs. - Pandas and NumPy are used for data preparation. - A clear understanding of modelling approaches and their suitability for different problems is necessary. - Strong communication skills and the ability to present technical concepts to diverse audiences are important. - The ability to collaborate across engineering, data science, product, and security teams is necessary. - Experience mentoring and guiding junior data scientists is preferred. - Agentic AI and LLM expertise is highly preferred. - Hands-on experience with agentic AI architectures, such as CrewAI, LangGraph, LangChain agents, tool-calling, and function-chaining, is preferred. - Experience building multi-step reasoning workflows, autonomous agents, or LLM-powered decision systems is preferred. - Understanding of prompt engineering, evaluation frameworks, LLM observability, and guardrails is preferred. - Experience working with AWS Bedrock, model selection, latency/cost trade-offs, and LLM deployment patterns is preferred. - AWS and cloud experience is highly preferr