Senior AI Engineer MLOps

Senior AI Engineer MLOps

Rapid7 is a publicly traded Cybersecurity company headquartered in Boston MA with 17 offices around the world. We are excited to be expanding our Global footprint into India and as a part of this growth we are building an AI Centre of Excellence. The global order relies on a free and open internet and cybercriminals have turned it into their playground. As they chase AI to increase the speed and scale of their attacks Rapid7 has been leveraging it to supercharge our cybersecurity detections and triage alerts quickly. For decades weve been using AI technology to power risk and threat analysis to detect attacks earlier and reduce response time.

Rapid7s AI Center of Excellence encompassing the full range of AI ML and data science is growing! As a leader in cybersecurity were on a new mission to use AI to accelerate threat investigation detection and response (D&R) capabilities of our Security Operations Center (SOC) for both conventional networks and cloud environments. The AI race to stay ahead of attackers is on and needs more than speed. Attack surfaces are getting broader every day and its important to optimize D&R with AI that can identify signals in a sea of noise. Further were embracing generative AI and researching novel ways LLMs can add value for our customers in D&R.

Were seeking the best AI ML and data science talent to build systems for detecting patterns and anomalies humans cant and which rulebased detections miss. In this Senior AI Engineer role you will help and build the MLOps tooling and infrastructure for our growing AI team. Theres a ton of work to do and we would love you to join us!

About the Team

Our AI Centre of Excellence team partners with our Detection and Response teams in enabling customers to assess risk detect threats and automate their security programs. Rapid7 also has a bestofbreed managed SOC offering known as MDR where teams of analysts are retained to carry out crucial D&R work on behalf of our customers. The positive impact of AI in D&R will be felt right across our customer base. The AI CoEs goals are ambitious and we need dynamic people with a desire to be part of something big. If you want a career move where you can grow and make an impact with AI this is it.

We ensure AI ML and data science are applied in a meaningful way to add customer value best achieve business objectives and deliver ROI. Unnecessary complexity is avoided and we adopt a creative fastfail highly iterative approach to accelerate ideas from proofofconcept to go or nogo. Our current capabilities are built on 20 years of threat analysis and opensource communities with 40 AI patents granted and 20 pending. However were just getting started!

The makeup of the group is such that our technical skills complement one another. No one can be an expert in everything; we share our AI ML and data science knowledge between ourselves plus are creating an inhouse AI Learning & Development plan. The AI CoE also contributes on occasion to new external AI policy initiatives with recognised bodies like NIST. In fact wed be delighted if youre open to publishing and presenting your best work with Rapid7 at top conferences around the world and in journals. Our current AI team has a track record of publishing awardwinning research at the likes of AISec at ACM CCS and with IEEE; we realise the benefits publishing can bring to an AI career and the confidence it inspires in our customers.

About the Role

Rapid7 is seeking a Senior AI Engineer to join our team as we expand and evolve our growing AI and MLOps efforts. You should have a strong foundation in applied AI R&D software engineering and MLOps and DevOps systems and tools. Further youll have a demonstrable track record of taking models created in the AI R&D process to production with repeatable deployment monitoring and observability patterns. In this intersectional role you will deftly combine your expertise in AI/ML deployments cloud systems and software engineering to enhance our product offerings and streamline our platforms functionalities.

In this role you will:

Interdisciplinary Collaboration

• Collaborate closely with engineers and researchers to refine key product and platform components aligning with both user needs and internal objectives.

• Actively contribute to crossfunctional teams focusing on the successful building and deployment of AI applications

Data Pipeline Construction and Lifecycle Management

• Contribute to and maintain data pipelines manage the data lifecycle and ensure data quality and consistency throughout.

Model Development Validation and Maintenance

• Help to build validate and continuously improve machine learning models manage concept drift and ensure the reliability of deployed systems.

Knowledge and Expertise Sharing

• Thoroughly document research findings methodologies and implementation details.

• Share expertise and knowledge consistently with internal and external stakeholders nurturing a collaborative environment.

ML Deployment

• Implement monitor and manage ML services and pipelines within an AWS environment employing tools such as Sagemaker and Terraform.

• Assure robust implementation of ML guardrails leveraging frameworks like NVIDIA NeMo and managing all aspects of service monitoring.

• Develop and deploy accessible endpoints including web applications and REST APIs while maintaining steadfast data privacy and adherence to security best practices and regulations

Software Engineering

• Contribute to the development of core API components to enable interactions with LLMs.

• Craft and optimize conversational interfaces capitalizing on the capabilities of LLMs.

• Conduct API and interface optimization with a productfocused approach ensuring performance robustness and user accessibility are paramount

Continuous Improvement

• Embrace agile development practices valuing constant iteration improvement and effective problemsolving in complex and ambiguous scenarios.

The skills youll bring include:

• A minimum of 5 years experience as a Software Engineer with 35 years focussed on gaining expertise in ML deployment (especially in AWS)

• A solid technical understanding in the following is required:

  • building APIs and/or interfaces paired with adept coding skills in Python and TypeScript

  • containerization and DevOps

  • CI/CD tooling Docker Kubernetes and have prior experience developing APIs with Flask or FastAPI

  • experience deploying LLMs managing advanced compute resources like GPUs and navigating data collection for metrics and finetuning from LLMbased systems

• Demonstrate exemplary analytical and problemsolving capabilities particularly in decomposing complex problems into manageable parts and devising innovative solutions

• Strong communication skills with the capacity to convey intricate ideas effectively. Able to explain hardtounderstand topics to different audiences working to build consensus and writing up work clearly

• Maintain high standards of engineering hygiene embracing best practices and an agile development mindset

• Bring a positive cando solutionoriented mindset welcoming the challenge of tackling the biggest problems with a bias for action

• Are persistent and consistent being able to systematically tackle complex use cases headon

• Enjoy working in a fastpaced environment sometimes with multiple projects to juggle simultaneously

• Understand the highly iterative nature of AI development and the need for rigour. Appreciate the importance of thorough testing and evaluation to avoid silent failures

• Are a great teammate to help peers become stronger problem solvers communicators and collaborators

• Have a curiosity and passion for continuous learning and selfdevelopment

• Stay receptive to new ideas listen to suggestions from colleagues carefully considering and sometimes adopting them

• Realise the importance of wider ethical and risk considerations with AI

Experience with the following would be advantageous:

• Have previous experience with NLP and ML models understanding their operational frameworks and limitations.

• Have previous experience deploying LLMs managing advanced compute resources like GPUs and navigating data collection for metrics and finetuning from LLMbased systems

• Possess proficiency in implementing model risk management strategies including model registries concept/covariate drift monitoring and hyperparameter tuning.

• Experience in designing and integrating scalable AI/ML systems into production environments.

We know that the best ideas and solutions come from multidimensional teams. Thats because these teams reflect a variety of backgrounds and professional experiences. If you are excited about this role and feel your experience can make an impact please dont be shy apply today.

About Rapid7

At Rapid7 we are on a mission to create a secure digital world for our customers our industry and our communities. We do this by embracing tenacity passion and collaboration to challenge whats possible and drive extraordinary impact.

Here were building a dynamic workplace where everyone can have the career experience of a lifetime. We challenge ourselves to grow to our full potential. We learn from our missteps and celebrate our victories. We come to work every day to push boundaries in cybersecurity and keep our 10000 global customers ahead of whatevers next.

Join us and bring your unique experiences and perspectives to tackle some of the worlds biggest security challenges.

#LIMS