Netskope
Cloud Security
DistinguishedEngineer,MachineLearning
“Distinguished Engineer, Machine Learning at Netskope. Skills: AI technical strategy, architecture and deployment of large-scale, production-grade AI/ML solutions, AI/ML inference systems, LLM serving technologies, End-to-End AI Lifecycle, AI models in production. Serve as the chief architect of our AI technical strategy. Lead the architecture and deployment of large-scale, production-grade AI/ML solutions for our Secure Access Service Edge (SASE) architecture”
Industry & Context.
solve the industry's most challenging AI latency, scalability, and cloud security problems today
What They're Looking For.
Must Have
12-15+ years of industry experience (or equivalent combination of an Advanced technical degree + experience) in architecting and developing AI/ML solutions, Fluency in the modern AI stack with proven, hands-on experience optimizing cutting-edge LLMs in production environments, Ability to translate complex technical architectures and concepts between CXOs, non-technical stakeholders, and Data Scientists, Energetic self-starter with a true startup spirit, demonstrated ability to influence without authority, and the willingness to wear multiple hats to deliver end-to-end solutions in a dynamic, fast-paced environment, Advanced degree (Masters or PhD) in Computer Science, Electrical Engineering, or equivalent technical degree
Nice to Have
preferably on large-scale security products or services, deep knowledge of vLLM, SGLang, and KV Cache optimization
What You'll Do.
Serve as the chief architect of our AI technical strategy
Lead the architecture and deployment of large-scale
production-grade AI/ML solutions for our Secure Access Service Edge (SASE) architecture
Define the AI Transformation Roadmap
Drive the overarching AI/ML technical strategy
ruthlessly prioritizing architectural choices to ensure highly scalable
and production-grade systems
Architect Production-Grade Inference Systems
and deploy highly scalable AI/ML inference systems
leveraging the latest LLM serving technologies such as vLLM
and advanced KV Cache optimization to maximize throughput and minimize latency
Lead the End-to-End AI Lifecycle
Collaborate with ML scientists
and executive stakeholders to translate complex business requirements into industrialized enterprise solutions
Establish Rigorous AI Evaluation
Create strict 'Report Cards' for AI models in production
ensuring models are robust
and well-documented by measuring accuracy
and security relevance before deployment
How You'll Work.
Team & Collaboration
Collaborate with ML scientists, engineers, and executive stakeholders; working alongside top-tier engineers, researchers, and machine learning scientists
Communication Scope
Ability to translate complex technical architectures and concepts between CXOs, non-technical stakeholders, and Data Scientists
Process & Methodology
ruthlessly prioritizing architectural choices
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