Software Engineer III – AI Security

F5 · Enterprise · Hyderabad, India

Software Engineer III role focused on designing and building AI-enhanced tooling and intelligent systems for safe and resilient AI workflows. The role involves creating platforms for embedding safe logic, enforcing secure behavior, and maintaining operational quality using techniques like semantic chunking and agent orchestration. It requires collaboration with product, security, and infrastructure teams to deliver reliable and safe intelligent software.

What you'd actually do

  1. Design and implement components for AI-enhanced tooling that monitors, evaluates, and improves software behavior with an emphasis on safety and resilience across distributed systems.
  2. Build and maintain systems that leverage semantic chunking, language model agents, and automated reasoning to support developer workflows with contextual awareness and guidance.
  3. Collaborate with cross-functional teams — product, security, SRE — to translate high-level needs into technical designs and robust implementations.
  4. Contribute to safe AI practices by defining interfaces and logic that help prevent and detect unsafe code constructs or misconfigurations without relying on simple pattern matching.
  5. Partner with DevOps and CI/CD engineers to integrate quality feedback loops, instrumentation, and runtime guards into build, deploy, and release pipelines.

Skills

Required

  • Python, Go, or TypeScript
  • distributed system design
  • asynchronous workflows
  • microservices architectures
  • AI/ML concepts
  • cloud environments (AWS, GCP, Azure)
  • container orchestration (Kubernetes)
  • APIs
  • message buses
  • streaming data (Kafka, Pub/Sub)
  • secure development practices
  • runtime policy enforcement
  • defensible system design

Nice to have

  • semantic chunking
  • structural representations of code/data
  • LLMs
  • agent frameworks
  • developer productivity tools
  • platform services
  • formal methods
  • symbolic analysis
  • safety-constrained execution environments
  • observability tooling (metrics, logs, traces)
  • runtime dashboards

What the JD emphasized

  • safe logic
  • enforce secure behavior
  • maintain operational quality
  • safe AI practices
  • prevent and detect unsafe code constructs or misconfigurations without relying on simple pattern matching
  • runtime guards

Other signals

  • AI-enhanced tooling
  • intelligent systems
  • safe, resilient AI workflows
  • agent orchestration
  • trustworthy code pipelines
  • safe AI practices
  • runtime guards