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    Home/Jobs/Staff Software Engineer, AI & Automation

    Staff Software Engineer, AI & Automation

    Gladly

    United States
    8+ years
    Today
    ₹183–246 LPA
    Full-time
    Remote

    Skills Required

    LLM
    OpenAI
    Claude
    TypeScript
    React
    Go
    PostgreSQL
    LangSmith
    Datadog
    SumoLogic
    API
    Integration platforms

    Description

    Gladly is looking for a Staff Software Engineer, AI & Automation to join their AI Knowledge team. The ideal candidate will have 8+ years of full-stack engineering experience and a track record of shipping large-scale products. This is a high-autonomy role with a focus on technical leadership.

    Company: Gladly

    Role: Staff Software Engineer, AI & Automation

    Location: US, Remote

    Experience:

    • 8+ years of full-stack engineering experience
    • Proven Staff or Principal-level ownership
    • Daily, hands-on use of AI coding tools

    Key Skills:

    • TypeScript
    • React
    • Go
    • PostgreSQL
    • AI coding tools
    • LLMs

    Qualification:

    • Strong track record of turning ambiguous ideas into shipped product

    Role Focus:

    • Own the integration architecture
    • Build the connective tissue for AI
    • Solve product-level problems
    • Ship at a high bar, fast
    • Mentor and raise the bar

    Additional responsibilities:

    • Collaborate across the company

    Nice to have:

    • Experience building integration platforms
    • Familiarity with protocols like MCP
    • Hands-on experience with LLMs
    • Familiarity with operational tools
    • Open source work

    Other:

    • $183,000 to $246,000 annual salary + equity + benefits
    • Competitive salaries, stock options, and comprehensive benefits
    • Generous paid time off, parental leave, and home office stipends
    • A fully remote work environment

    Prepare for this role

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    Generative AI with Large Language Models

    Coursera

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    Large Language Models: Application through Production

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    Production-focused LLM course covering deployment, monitoring, and scaling.

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