AI Engineer, Agentic Systems (Quality Engineering)
Palo Alto Networks · Posted Sep 24
Network security, cloud security, AI threat defense, and security operations platforms
Bengaluru, KA, INFull-timeOnsiteMid Level1–4 years₹25.0L–₹45.0L yearly100+ applicants
CybersecurityNetwork SecurityCloud SecurityArtificial IntelligenceInformation TechnologyPublic Company
Full time
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About the role
Palo Alto Networks is a cybersecurity company on a mission to protect our digital way of life, weaving AI into everything they do. This role is an early-in-career AI Engineer on the IT Quality Engineering Automation team in Bangalore, building and shipping custom Agentic AI applications across an enterprise IT landscape. The team is transitioning from traditional QA to an AI-First engineering culture, with this role working on LLM orchestration, autonomous agent loops, tool calling, and RAG against real enterprise infrastructure including Salesforce, SAP, and custom licensing and provisioning platforms.
What you will do
- Build agentic features: Develop and maintain agent workflows using LangGraph, ADK, LangChain, AutoGen or CrewAI, including planner/executor loops, state handling and failure recovery.
- Enable cross-system integration and tool calling: Connect agents across enterprise applications, including: Salesforce: REST/SOAP APIs, Webhooks, Platform Events, Apex Invocable Methods, SOQL and Flows. SAP: OData/BAPI services and business-object events. Custom applications: Licensing, Entitlement, AI Custom Quoting, Provisioning and ITSM through REST APIs, webhooks and message queues.
- Build context grounding and RAG pipelines: Use vector stores such as pgvector, Pinecone and Weaviate to provide LLMs with accurate, current enterprise context.
- Define tools and schemas: Create well-specified tool definitions and JSON function schemas, including MCP tools, to support deterministic tool calls and traceability back to Jira.
- Build evaluation suites: Develop and run golden-set evaluations to validate improvements to agents and prompts before release.
- Implement guardrails and human-in-the-loop workflows: Establish safety controls, rate limiting, fallbacks and clear approval handoffs, especially when agents write to systems of record.
- Enable observability: Instrument agents with tracing and metrics covering execution paths, latency, context usage and token costs, and use findings to improve performance.
- Apply responsible AI practices: Follow company policies on intellectual property and data privacy, keep secrets in managed stores and require explicit opt-in for steps that send content to external services.
- Collaborate across functions: Partner with Salesforce, SAP and platform architects, QE leads, product managers and security teams to deliver reliable AI features that meet business, quality and enterprise compliance requirements.
- Learn the enterprise landscape: Build an understanding of transaction flows from AI Custom Quoting and order capture through licensing, entitlement, provisioning and service management. Prior knowledge is not required, but proactive learning is expected.
- Share knowledge: Contribute working examples, reusable tools and reviewed code to strengthen AI fluency across engineering teams.
Skills used in this role
PythonTypeScriptNode.jsLangGraphADKLangChainAutoGenCrewAISalesforceSAPREST APIsSOAP APIsWebhooksPlatform EventsApexSOQLFlowsODataBAPIJSONMCPJirapgvectorPineconeWeaviateRAGLLMGitCI/CDCursorClaude CodeGitHub CopilotGCPAWSAzureQdrantChromaDBGraph RAGCorrective RAGHyDEApache AGENeo4jMCPOAuthSOQL/SOSLREST/Composite/Bulk APIsConnected AppsIDocsRFCBAPI/RFCITSMAI Custom QuotingLicensingEntitlementProvisioningService Management
What the employer is looking for
- Experience: 1–4 years of professional software engineering experience or equivalent demonstrable capability.
- Programming: Strong proficiency in Python or TypeScript/Node.js, with sound fundamentals in data structures, testing and code review.
- Backend fundamentals: Practical experience building and consuming REST APIs, working with relational databases and handling asynchronous or queue-based processing.
- Hands-on LLM experience: Demonstrable experience with LLM APIs, including prompting, function/tool calling and structured outputs, through professional work, internships, open-source contributions or substantial personal projects.
- Agentic or RAG exposure: Built at least one working agent or retrieval pipeline end to end, with the ability to explain its design, failures and improvements.
- Integration skills: Comfortable reading unfamiliar enterprise API documentation and determining how to integrate safely.
- Version control and CI: Comfortable using Git in a team environment and working with automated build and test pipelines.
- AI-assisted development: Daily working fluency with tools such as Cursor, Claude Code or GitHub Copilot, with the judgement to critically review generated output.
- Education: B.E./B.Tech/M.Tech in Computer Science, Computer Engineering or a related technical field, or equivalent practical experience.
Preferred qualifications
- Production agents: Experience deploying AI agents or LLM features into production, including multi-agent coordination or autonomous decision loops.
- Enterprise application knowledge: Exposure to one or more of the following: Salesforce: REST/Composite/Bulk APIs, SOQL/SOSL, Connected Apps, OAuth and Apex. SAP: OData, BAPI/RFC, IDocs or business-object events. Enterprise platforms: AI Custom Quoting, Provisioning, Licensing/Entitlement or ITSM platforms and their supporting distributed services.
- Vector databases and embeddings: Experience with semantic search, embedding generation and vector engines such as pgvector, Pinecone, Qdrant or ChromaDB.
- Advanced retrieval: Familiarity with Graph RAG, Corrective RAG, HyDE or agentic retrieval, and graph stores such as Apache AGE or Neo4j.
- Model Context Protocol: Experience authoring MCP servers, including tools, resources and prompts.
- Cloud and DevOps: Exposure to GCP, AWS or Azure, contai...
Benefits and support
- flexibility when it’s needed
About Palo Alto Networks
Palo Alto Networks is a global cybersecurity leader that delivers integrated platforms and services across network security, cloud security, and security operations. Leveraging advanced artificial intelligence and threat intelligence through Unit 42, the company helps tens of thousands of enterprise and government customers worldwide protect against sophisticated cyber threats.
- Industry
- Cybersecurity
- Company size
- 16000+ employees
- Founded
- 2005
- Location
- Santa Clara, California, USA
- Funding stage
- Public Company
Funding
Public Company · $65.7M raised
Globespan Capital PartnersLehman Brothers
- 2008-11-01Series C$40M
Leadership
NA
Nikesh Arora
Chairman and Chief Executive Officer
LK
Lee Klarich
Chief Product and Technology Officer
DG
Dipak Golechha
Chief Financial Officer
MR
Meerah Rajavel
Chief Information Officer
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