Principal Software Engineer - Security/ML Platform Architect

We are a global team of innovators and pioneers dedicated to shaping the future of observability. At New Relic, we build an intelligent platform that empowers companies to thrive in an AI-first world by giving them unparalleled insight into their complex systems. As we continue to expand our global footprint, we're looking for passionate people to join our mission. If you're ready to help the world's best companies optimize their digital applications, we invite you to explore a career with us!

Your opportunity

At New Relic, we provide businesses with a state-of-the-art observability platform, leveraging advanced technologies to deliver real-time insights into the performance of software applications and infrastructure. Our platform delivers deep insights into application performance, enabling businesses to make data-driven decisions with confidence. As an industry innovator, New Relic is committed to harnessing the power of artificial intelligence and machine learning to enhance observability and predictive analytics while ensuring the integrity and security of our solutions.

Our team values creativity, collaboration, and cutting-edge technology, recognizing the vital role AI plays in transforming digital operations. We are seeking a Principal Software Engineer with deep expertise in cloud platform security to lead the strategy and design of our enterprise security efforts with a strong focus on AI engineering. We aim to revolutionize our internal tooling with AI to enhance developer productivity, enabling our engineers to build and innovate faster while ensuring robust security and seamless integration throughout the development lifecycle.

What you’ll do

• Enterprise Security Strategy and Design: Lead the design and implementation of enterprise-grade security solutions and cloud-agnostic frameworks, including IAM, NACL, SSO, and RBAC, to ensure robust security across various cloud provider infrastructure. Support FedRAMP compliance by conducting security reviews and risk assessments and establishing security guardrails to maintain a strong security posture while enabling rapid development.
• DevSecOps Integration: Champion DevSecOps practices by seamlessly integrating security controls into CI/CD pipelines and automating security workflows, including scanning and remediation. Enable self-service security tools and Infrastructure as Code (IaC) policy enforcement to enhance development efficiency and maintain security standards.
• Secure AI Application Design: Direct the design and development of secure, scalable AI applications within cloud environments, focusing on high performance, accuracy, and reliability. Establish benchmarks and metrics to evaluate the security and performance of agents and agentic frameworks.

This role requires

• 15+ years of relevant technology experience with 8+ years of architecting platforms and frameworks that operate at a high level of scale 
• 5 years of experience in cloud platform security with a strong focus on AI and machine learning engineering
• Proven experience implementing DevSecOps practices in enterprise environments
• Experience building and maintaining CI/CD pipelines with integrated security controls
• Experience with security testing automation tools (SAST, DAST, SCA, container scanning)
• Extensive experience with at least one major cloud provider (AWS, Azure, or GCP)
• Proven experience designing and implementing zero-trust security models
• Strong understanding of infrastructure as code (e.g., Terraform, CloudFormation, Pulumi)
• Experience with containerization technologies (Docker, Kubernetes) and their security implications
• Knowledge of industry security standards and compliance frameworks (e.g., SOC2, ISO 27001, NIST)
• Experience with FedRAMP compliance requirements and security control implementation
• Experience with secrets management solutions (HashiCorp Vault, AWS Secrets Manager, etc.)
• Strong coding skills in Python, Java, or C#, with a solid grasp of AI-focused libraries such as TensorFlow, PyTorch, Hugging Face Transformers, and other frameworks that support generative AI development.

• Knowledge of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Large Language Models (LLMs) like OpenAI’s GPT series, BERT, and Transformer architectures. This involves understanding their architecture, training methods, and applications.

• Familiarity with cloud platforms that offer specific support for deploying and scaling generative models and RAG knowledge base
• Ability to align AI/ML initiatives with broader business goals and outcomes, understanding how AI can dri