Principal Software Engineer - AI ADC

Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 7+ years of experience in software engineering and systems architecture.
  • Strong expertise in Application Delivery Controllers (ADC), Server Load Balancing (SLB), DNS/GSLB.
  • Proven experience in developing and implementing AI/LLM systems.
  • Familiarity with vector databases, model registries, and ML observability.
  • Ability to mentor teams and communicate complex ideas effectively.

Responsibilities

  • Lead the architecture and development of AI Gateway components.
  • Design and implement LLM-aware load balancing solutions.
  • Develop enhancements for ADC and GSLB platforms for AI workloads.
  • Integrate systems with vector databases and ML observability tools.
  • Contribute to context-aware routing and anomaly detection features.
  • Drive system architecture including data and control planes.
  • Collaborate with AI research teams to align technology with business goals.

Benefits

  • Hybrid work arrangement providing flexibility.
  • Emphasis on mentorship and career growth opportunities.
  • Cutting-edge projects at the intersection of AI and infrastructure technology.
Full Job Description
Principal Software Engineer - AI ADC

This role combines deep domain expertise in Application Delivery Controllers (ADC), Server Load Balancing (SLB), DNS/GSLB, with practical experience in AI/LLM systems, to build intelligent routing, inference-aware load balancing, and Layer-8 contextual decision engines for AI workloads.

The ideal candidate is a senior staff engineer who can drive architecture, design, prototyping, and execution-working closely with product management, platform teams, and AI strategy groups.

Key Responsibilities

  • Lead architecture and development of AI Gateway components for intelligent routing of LLM/AI application traffic


  • Design and implement LLM-aware load balancing, incorporating semantic, token, latency, and model-level insights into traffic decisions


  • Develop enhancements for ADC and GSLB platforms to support AI workloads, including:


  • Token-aware rate limiting


  • Inference latency-based routing


  • AI model endpoint discovery and health checks


  • Integration with vector databases, model registries, and ML observability systems


  • Contribute to advanced features including Layer-8 context-aware routing, adaptive traffic shaping, and AI-driven anomaly detection


  • Drive system-wide architecture: data plane, control plane, configuration, and distributed state management


  • Mentor engineering teams on AI workload behaviors, traffic characteristics, and tuning strategies


  • Collaborate with AI research and platform teams to align architectural decisions with product roadmap.


  • Create guidance on performance optimization, benchmarking, and scaling for global multi-node deployments


AI Use Guidelines for Interviews: Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.

Targeted compensation guideline: Up to 230K OTE. Compensation will vary based on number of factors, including market demand for specific skills, role type, job level, and individual qualifications. Final salary offers are determined by considerations including, but not limited to, subject matter expertise, demonstrated skill level, relevant experience, geographic location, education, certifications, and training.

#LI-AN1 - Hybrid

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