Embed with enterprise retail customers and partners to understand their technical environments, data systems, and business workflows.
Design, build, and deploy AI and agentic solutions tailored to each customer's specific infrastructure and needs, across Instacart's full platform suite.
Extend, adapt, and integrate Instacart's core AI capabilities to fit customer ecosystems, even when those systems are undocumented or non-standard.
Maintain a close working relationship with R&D: participate in product reviews, flag platform gaps encountered in the field, and propose concrete changes that would make the platform work better for enterprise customers.
Collaborate closely with the AI Solutions Architect to translate domain requirements and architecture into working software.
Serve as the primary technical point of contact for customers during implementation engagements.
Requirements
10+ years of software engineering experience with a demonstrated ability to write and ship production-quality code.
Hands-on experience building with LLM APIs, function calling, tool use, agent frameworks, or RAG pipelines—you have shipped something agentic that real users depended on.
Proficiency in Python or another scripting/backend language commonly used in data-intensive environments.
Proven ability to integrate with messy, heterogeneous enterprise data environments — undocumented APIs, schema mismatches, auth complexity, and legacy systems that do not behave the way the docs claim.
Direct experience with external enterprise customers or technical stakeholders in a consulting or customer-facing capacity — earning trust in the room matters as much as writing code.
Strong working knowledge of retail and e-commerce system fundamentals.
Strong communication skills — able to translate field learnings and technical findings into actionable input for R&D and product teams.
Comfort operating in ambiguous, fast-moving environments where the process is not fully defined.
Prior work in a forward deployed, embedded engineering, or professional services capacity (preferred).
Background at a startup or early-stage company where you wore multiple hats (preferred).