Sr Principal AI Software Engineer / Generative AI Applications Architect (NetSec)

Palo Alto Networks

2d•Onsite

About The Position

Your Career Palo Alto Networks is looking for a highly experienced, hands-on Senior Principal AI Software Engineer to architect and build the next generation of Generative AI applications and agentic systems across the enterprise. In this role, you will act as a senior technical leader responsible for defining and driving the architecture of AI-native applications that solve complex business problems through intelligent workflows, reasoning systems, and scalable distributed services. You will move beyond traditional software engineering to design and implement production-grade vertical AI applications that combine large language models, retrieval systems, structured and unstructured data reasoning, workflow orchestration, evaluation frameworks, and enterprise guardrails. You will partner closely with product, platform, security, data, and application teams to turn ambiguous opportunities into practical architectures and working systems. This is a deeply hands-on role for someone who can operate at both the strategy and implementation layers — shaping technical direction while also prototyping, reviewing, and guiding critical components into production.

Requirements

BS, MS, or PhD in Computer Science, Engineering, or a related field, or equivalent practical experience.
10+ years of experience building large-scale distributed systems, platforms, or enterprise applications, with significant hands-on engineering depth.
Proven experience designing and shipping production AI or LLM-powered applications, including agentic workflows, RAG systems, intelligent automation, or AI-native business applications.
Strong experience with Python and at least one additional language such as Go or Java.
Strong understanding of modern agent orchestration frameworks such as LangGraph or equivalent approaches for stateful, multi-step, tool-using AI systems.
Strong experience with Temporal or similar durable workflow orchestration systems for long-running, reliable, and resilient execution.
Experience designing reasoning systems that combine structured and unstructured data, including text-to-SQL, retrieval-augmented workflows, API-based reasoning, and workflow-based decision systems.
Deep experience with vector stores, embeddings, retrieval pipelines, and semantic search architectures.
Strong experience with search platforms such as Elasticsearch or OpenSearch, including relevance tuning, indexing strategies, filtering, full-text retrieval, and hybrid search design.
Experience building LLM evaluation frameworks, including offline benchmarking, regression testing, trace analysis, output quality scoring, and KPI-based measurement in production.
Experience implementing AI guardrails and safety controls, including data handling boundaries, policy-based filtering, grounding validation, and safe tool invocation patterns.
Strong knowledge of distributed systems fundamentals, cloud-native architecture, reliability engineering, observability, and performance optimization.
Excellent communication and collaboration skills, with the ability to influence senior engineers, architects, product leaders, and business stakeholders.
Strong ownership mindset with the ability to thrive in ambiguity, rapidly evaluate new technologies, and drive from concept to production.

Nice To Haves

Experience building vertical AI applications for domains such as cybersecurity, IT operations, customer support, sales, finance, legal, or internal enterprise productivity.
Experience with hybrid retrieval and ranking techniques that combine keyword, metadata, vector, and semantic signals.
Experience with evaluation-driven development for LLM systems and agents.
Experience with AI observability platforms, prompt and workflow tracing, and operational debugging for production AI systems.
Experience building shared AI infrastructure or internal AI platforms used by multiple product or application teams.
Familiarity with cloud-native systems running on platforms such as GCP or AWS, and with technologies such as Kubernetes, Terraform, and modern CI/CD pipelines.
Experience balancing model quality, latency, cost, and safety trade-offs in high-scale enterprise environments.

Responsibilities

Architect and build enterprise-grade Generative AI applications that combine LLMs, retrieval, structured data access, search, orchestration, and workflow automation.
Design agentic systems and multi-step reasoning workflows using frameworks such as LangGraph or equivalent, with clear control over state, memory, tool invocation, and human-in-the-loop checkpoints.
Build durable, fault-tolerant orchestration for long-running AI and business workflows using Temporal or similar workflow execution platforms.
Define architectures that reason effectively across both structured and unstructured enterprise data, including relational data, APIs, knowledge bases, event streams, and document stores.
Lead the design of retrieval pipelines that include lexical search, semantic search, vector search, metadata filtering, reranking, and hybrid retrieval strategies.
Architect and optimize AI search experiences using platforms such as Elasticsearch, OpenSearch, and vector databases to support high-relevance, large-scale enterprise applications.
Establish evaluation frameworks for LLM and agentic systems, including offline evals, regression suites, scenario-based testing, trace-level diagnostics, human review loops, and online quality metrics tied to business outcomes.
Define and implement guardrails for AI applications, including prompt injection defenses, sensitive data protection, policy enforcement, grounding checks, output controls, and secure tool access boundaries.
Drive reference architectures and reusable platform patterns for model routing, prompt and workflow versioning, observability, experimentation, and safe production rollout.
Lead technical design reviews, mentor senior engineers, and raise the engineering bar for AI application reliability, maintainability, and security.
Stay deeply involved in execution by prototyping critical workflows, evaluating frameworks and platforms, and helping teams solve the hardest production issues.