Job Description:
Key Responsibilities
- Architecture & Design
- Design end-to-end secure AI/ML solutions using Cisco AI Defender and NeMo frameworks.
- Architect scalable, production-grade LLM and generative AI systems across hybrid and multi-cloud environments.
- Define secure reference architectures for AI workloads, including data pipelines, model training, and inference layers.
- AI Security & Governance
- Implement AI-specific security controls including model integrity, prompt injection defense, data leakage prevention, and adversarial attack mitigation.
- Leverage Cisco AI Defender to monitor, detect, and respond to AI-related threats and anomalies.
- Ensure alignment with enterprise security frameworks (NIST, ISO 27001, Zero Trust).
- Platform Engineering & Integration
- Integrate NeMo and related libraries (NeMo Guardrails, Triton Inference Server, CUDA, TensorRT) into enterprise platforms.
- Collaborate with DevOps and MLOps teams to operationalize AI models securely.
- Build reusable architecture patterns and automation for AI deployment pipelines.
- Stakeholder Engagement
- Partner with security, infrastructure, data science, and executive stakeholders to define AI strategy and roadmap.
- Translate complex technical concepts into business-aligned solutions and risk considerations.
- Lead technical workshops, design sessions, and architecture reviews.
- Risk, Compliance & Monitoring
- Conduct threat modeling and risk assessments specific to AI/LLM deployments.
- Establish observability and monitoring strategies for AI systems (model drift, misuse, anomalies).
- Ensure compliance with regulatory and data privacy requirements.
Required Qualifications
- 7+ years of experience in Solutions Architecture, Security Architecture, or AI/ML Engineering
- Deep hands-on experience with:
- Cisco AI Defender (or equivalent AI security platforms)
- NeMo ecosystem (NeMo, NeMo Guardrails, Triton, CUDA)
- Strong background in information security, including:
- Zero Trust Architecture
- Identity & Access Management (IAM)
- Data protection and encryption
- Experience designing and deploying LLM / Generative AI solutions in enterprise environments
- Proficiency in cloud platforms (AWS, Azure, or GCP)
- Strong understanding of MLOps / DevSecOps practices Preferred Qualifications
- Experience with AI governance frameworks and responsible AI practices
- Familiarity with vector databases, RAG architectures, and model fine-tuning
- Certifications such as CISSP, CCSP, AWS/Azure Architect, or NVIDIA certifications