Job Abstract

This role with partner with the existing Security Experts to validate that security controls are enabled throughout the lifecycle of traditional Machine Learning and Generative AI phases of model development. The security controls will be defined from the early experimentation phases to model fine-tuning to model deployment and ongoing operational governance.

Key job responsibilities
Lead the development of security guidance on the use of AI/ML, particularly generative AI
Collaborate with security experts to validate and recommend security controls applicable for all phases of AI/ML/Gen AI development lifecycle
Design, build, test, and help deploy ML and generative AI solutions that have measurable business and customer impact in security.
Interact with internal and external customers to understand their business problems and help them in implementation of their generative AI and ML solutions
Facilitate discussions with senior leadership regarding technical / architectural trade-offs, best practices, and risk mitigation
Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder
Create detailed security documentation of solutions using reference architectures and implementation/configuration guidance
Collaborate with AI/ML peers to research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges
Provide customer and market feedback to Service and Engineering teams to help define product direction
Work with a cross section of AI experts to develop solutions that will be piloted with customers for their production workloads

We are open to hiring candidates to work out of one of the following locations:

Herndon, VA, USA

- Bachelor's degree in computer science, engineering, mathematics or equivalent
- Experience building models with deep learning frameworks like MXNet, Tensorflow, Keras, Caffe, PyTorch, or similar
- 5+ years of relevant experience in developing and evaluating deep learning models and/or systems, including batch and real-time data processing
- Experience with two or more of prompt engineering, retrieval-augmented generation (RAG), vector databases, or LLM frameworks such as Hugging Face, Langchain or LlamaIndex.
- 3+ years of experience in security architecture or engineering including application security, secure SDLC, or cloud security

- Graduate degree (MS or PhD) in computer science, data science, or related technical, math, or scientific field
- Experience in building Generative AI applications and large foundation models preferably using AWS services such as Sagemaker, Bedrock, Amazon Q, Kendra, OpenSearch, and Neptune
- Scientific thinking and the ability to invent; a track record of thought leadership and contributions that have impacted products and customers
- Familiarity and implementation experience with enterprise security solutions such as web application firewalls, IDS/IPS, SIEM, DLP, DDOS mitigation
- Understanding architectural implications of meeting industry standards such as PCI DSS, ISO 27001, HIPAA, NIST AI RMF, OWASP Top 10 for LLM, ISO/IEC 42001, and NIST/DoD frameworks

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