AI Engineer, Agentic Systems (Quality Engineering)

Mployee.me; Bengaluru; On-site; INR 3,000,000 - 5,500,000; Full time

Job summary

Palo Alto Networks is hiring a Staff Software Engineer in Bangalore to anchor AI-driven QA and SDLC automation. You will design and implement agentic systems across Salesforce, SAP and custom ITSM platforms, leveraging LLMs, vector stores and enterprise APIs.

Join an AI-first culture and help scale automated testing and observability. You will work with LangGraph, LangChain, AutoGen and related tools to build robust agentic flows, grounding context and ensuring safe, auditable executions across

Qualifications

• 1-4 years of professional software engineering experience or demonstrable capability.
• Strong proficiency in Python or TypeScript/Node.js, with solid fundamentals in data structures, testing and code review.
• Hands-on experience with REST APIs, relational databases and async/queue processing.
• Experience with LLM APIs, including prompting, function/tool calling and structured outputs.
• Experience building/operating AI agents or RAG retrieval pipelines end-to-end.
• Ability to read enterprise API docs and integrate safely.
• Proficient in Git, CI pipelines and collaborative development.
• Educational background in CS or equivalent practical experience.

Responsibilities

• Build agentic features and workflows using LangGraph, ADK, LangChain, AutoGen or CrewAI.
• Enable cross-system integration and tool calling for Salesforce, SAP, and custom apps.
• Develop context grounding and RAG pipelines with vector stores like pgvector, Pinecone or Weaviate.
• Define tool definitions and JSON schemas to support deterministic calls and traceability.

Skills

Python

TypeScript/Node.js

LLM APIs

Git & CI

REST APIs

Agentic/RAG

Education

Bachelor's degree in Computer Science or related field

Tools

LangChain

LangGraph

AutoGen

CrewAI

pgvector

Pinecone

Weaviate

Job description

Job Summary

As a Staff Software Engineer in IT Quality Engineering Automation, you will be the technical anchor and visionary for our automated testing and next-generation SDLC ecosystem, operating directly from our Bangalore office. At Palo Alto Networks, we are transitioning from traditional QA to an AI-First engineering culture.

We are looking for an early-in-career AI Engineer to build and ship custom Agentic AI. Applications across an Enterprise IT landscape spanning Salesforce, SAP, custom. Licensing and Entitlement management applications, and AI Custom Quoting, Provisioning and ITSM platforms. You will work with modern generative AI - LLM orchestration, autonomous agent loops, tool calling and RAG - against real enterprise infrastructure, building agents that carry out multi-step business workflows, act on data across these systems through their APIs, and hand back to a human when they should.

At Palo Alto Networks we are moving towards an AI-First engineering culture, and you will not be starting from a blank page. You will build on our own internal AI platform tools for test generation, automation scripting, execution and quality reporting.

Agentic Systems Enterprise Integration

• 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.

Agent Quality, Safety Observability

• 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.

Collaboration Growth

• 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.

Qualifications

• 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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.