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AI Development Engineer; Enterprise AI Ecosystem

Job in Austin, Travis County, Texas, 78716, USA
Listing for: Qualcomm
Full Time position
Listed on 2026-03-11
Job specializations:
  • Software Development
    AI Engineer, Software Engineer
Salary/Wage Range or Industry Benchmark: 80000 - 100000 USD Yearly USD 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Position: Staff AI Development Engineer (Enterprise AI Ecosystem)

Company

Qualcomm Atheros, Inc.

Job Area

Engineering Group, Engineering Group >
Software Engineering

General Summary

Company Overview

Qualcomm is a world leader in wireless technology and the driving force behind the connected intelligent edge. To support our global engineering workforce, we are building a state-of-the-art Internal AI Ecosystem—a proprietary platform that empowers thousands of engineers with intelligent coding assistants, autonomous agents, and knowledge retrieval systems.

Role Overview

As a Staff AI Development Engineer, you will be a lead architect in the design and implementation of our proprietary internal AI framework. You will move beyond simple "prompt engineering" to build robust, stateful, and cyclic multi-agent systems using Lang Graph and Lang Chain. This is a high-impact software engineering role. You will not rely on rigid, off-the-shelf agent frameworks (like Auto Gen or CrewAI).

Instead, you will use low-level primitives to architect custom control flows, enabling LLMs to reason, plan, and execute complex tasks across Qualcomm’s massive engineering data landscape. You will define how agents maintain state, handle interrupts, and interact with internal APIs, setting the standard for AI application development across the company.

Key Responsibilities
  • Advanced Agentic Architecture

    Custom Workflow Design:
    Architect and implement complex, cyclic AI workflows using Lang Graph. Design state machines that enable agents to plan, execute, reflect, and retry tasks autonomously.

    State Management:
    Engineer robust persistence layers (checkpointers) to manage long-running agent conversations, enabling "human-in-the-loop" interactions where users can guide or correct agent behavior mid-flight.

    Multi-Agent Orchestration:
    Build custom orchestration layers where specialized agents (e.g., "Code Reviewer," "Doc Searcher," "Test Generator") collaborate via defined graph edges and conditional routing logic.

  • Framework & Tool Development

    Internal SDK Evolution:
    Contribute core features to Qualcomm’s internal Python AI SDK, simplifying how other teams consume LLMs, embeddings, and vector stores.

    Tool Abstraction:
    Develop standardized interfaces (based on Lang Chain tools) that allow agents to securely interact with enterprise systems (Jira, Git Lab, SQL Databases, Proprietary APIs).

    Graph-Based RAG:
    Go beyond simple semantic search by implementing Graph

    RAG and advanced retrieval strategies. Build pipelines that combine vector search with knowledge graph traversals to answer complex engineering queries.

  • Production Engineering & Scalability

    Asynchronous Python:
    Write high-performance, non-blocking Python code using asyncio to handle concurrent agent execution and high-throughput token streaming.

    API Design:
    Expose agent workflows via FastAPI microservices, ensuring strict typing (Pydantic), validation, and comprehensive open-api documentation.

    Evaluation & Observability:
    Design "LLM-as-a-Judge" evaluation pipelines to measure the efficacy of custom agent graphs. Implement tracing (e.g., Lang Smith or internal equivalents) to debug complex chains of thought.

  • Technical Leadership

    Pattern Definition:
    Define and document design patterns for agentic AI (e.g., Plan-and-Solve, ReAct, Map-Reduce) for the wider engineering organization.

    Mentorship:
    Serve as a technical mentor to Senior and Junior engineers, conducting rigorous code reviews and driving best practices in Python software design and AI safety.

Required Skills
  • 7+ years of professional Software Engineering experience.
  • Python Mastery:
    Expert-level proficiency in Python. Deep understanding of Async

    IO, Pydantic, decorators, and metaprogramming.
  • Lang Chain/Lang Graph:
    Proven, hands-on experience building applications with Lang Chain and Lang Graph. You must understand the difference between a Chain (DAG) and a Graph (Cyclic) and know when to apply each.
  • AI Fundamentals:
    Solid understanding of LLM mechanics (context windows, tokenization, temperature), embedding models, and vector stores.
  • System Design:
    Experience architecting distributed systems, microservices, and handling state in stateless environments (REST).
Preferred Qualifications
  • Master’s or PhD in Computer…
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