AI Solutions Engineer
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- Find high-value problems and design grounded AI solution concepts
- Prototype agentic solutions and integrate them into enterprise stacks
- Prove quality, secure production and drive measurable adoption
Benchmark: OpenAI Applied AI Architect + Anthropic Applied AI Architect, Enterprise Tech roles (current Sep 2026) Market refresh: executive/technical discovery, value-backed use-case portfolios, LLM/RAG/context, agents + MCP/tools, hands-on prototyping, enterprise integration, evals/reliability/security/privacy and sustained production adoption.
Who’s Hiring Right Now
US openings posted in the last 7 days at OpenAI — 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.
Find high-value problems and design grounded AI solution concepts
Executive/customer discovery + use-case prioritization
Coverage: Product Discovery & Customer Research / AI Use-Case Discovery
LLM behavior, context engineering + RAG grounding
Coverage: Generative AI / Prompt & Context Engineering / RAG
Prototype agentic solutions and integrate them into enterprise stacks
Agents, tools, MCP + workflow orchestration
Coverage: AI Agents / MCPs / Agentic Tools Platform
Solution architecture, reliability + integration tradeoffs
Coverage: Systems Architecture & System Design / APIs / AI Platform
Prove quality, secure production and drive measurable adoption
Evals, failure analysis + proof-of-value metrics
Coverage: AI Evals & Monitoring / Experimentation & Product Metrics
Security, privacy, governance + production adoption
Coverage: AI & Agent Governance and Safety / IAM / Change & Adoption
Role-Readiness Proof / Capstone
Act as the senior technical owner for an enterprise AI adoption: run executive and practitioner discovery; rank a use-case portfolio by value, feasibility and risk; define an architecture spanning model, app, data, integration, identity, security, privacy, governance, evaluation and deployment; build a Python/JavaScript proof of value with grounded retrieval plus an agent using tools/MCP; create eval and regression sets for quality, safety, latency and cost; enforce least privilege and data boundaries; instrument usage and reliability; prove business impact against a baseline; deliver an executive demo, production-readiness plan, adoption roadmap and product-feedback package.
Proof Rubric
- Trace customer evidence to prioritized use cases and measurable outcomes
- ship a working prototype, not slides
- defend model/context/RAG/agent and enterprise integration choices
- show eval, failure-analysis, latency/cost and reliability evidence
- enforce identity, security, privacy and safe tool boundaries
- quantify proof-of-value and adoption signals
- document production handoffs, rollback and governance
- communicate tradeoffs credibly from engineers to executives