AI Solution Developer
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- Diagnose the workflow, scope the system and build trustworthy data foundations
- Build agentic solutions, integrations and grounded knowledge systems
- Operate reliable AI workflows and transfer ownership so adoption persists
Benchmark: Stripe AI Solutions Developer / Finance Operational Excellence signal + OpenAI Forward Deployed Engineering + Salesforce Agentforce forward-deployed roles (current Sep 2026) Market refresh: workflow diagnosis; AI opportunity selection; rapid prototyping; SQL and data pipelines; agents and automation; knowledge/RAG; MCP and custom integrations; evals and validation; observability/incident response; user enablement, adoption and reusable playbooks.
Who’s Hiring Right Now
US openings posted in the last 7 days at Stripe — this role’s own benchmark employers — refreshed daily, as of 2026-11-01. Pulled directly from each company’s public job board; not exhaustive, and postings close fast — worth confirming the details on the employer’s own site before applying.
Diagnose the workflow, scope the system and build trustworthy data foundations
Workflow diagnosis, AI use-case selection + system scoping
Coverage: AI Use-Case Discovery / Process Management / Solution Architecture
SQL, data pipelines + data-quality validation
Coverage: SQL & Analytics / Data Engineering / Data Quality
Build agentic solutions, integrations and grounded knowledge systems
Agent architecture, workflow automation + tool execution
Coverage: AI Agents / Workflow Automation / Software Engineering
MCP/integrations, knowledge layers + production RAG
Coverage: MCP / APIs / RAG / Enterprise Knowledge
Operate reliable AI workflows and transfer ownership so adoption persists
Evals, observability, reliability + incident-ready operation
Coverage: AI Evals / Observability / LLMOps / Reliability
Adoption, enablement, human-AI operating model + handoff
Coverage: AI Adoption / Change Management / Training / Operating Models
Role-Readiness Proof / Capstone
Transform a real manual business workflow into a production-ready AI solution. Diagnose the current process and controls; define success and acceptance criteria; query and validate source data in SQL; build an agent with tools, MCP or APIs and a grounded knowledge layer; create evals and regression tests; instrument traces, quality, latency, cost and alerts; run a failure/incident drill; pilot with real users; then deliver SOPs, training, self-service ownership and an adoption/impact readout.
Proof Rubric
- Show a measurable workflow improvement, not an AI demo: traceable requirements
- trustworthy SQL/data logic
- sound agent/tool boundaries
- grounded retrieval
- reliable integrations
- eval coverage and failure analysis
- observability and recovery
- security/control awareness
- user feedback and adoption evidence
- maintainable documentation and handoff
- quantify cycle time, quality, cost, error-rate or capacity impact and explain when a simpler non-agent solution is better