Mistral AI Engineer / Applied AI & Enterprise Agents Engineer
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- Build production Mistral applications with disciplined full-stack and AI-assisted engineering
- Ground agents in enterprise data and compose durable workflows with governed tools
- Customize and operate secure Mistral systems across private and production environments
Benchmark: Mistral Applied AI Engineer + current Studio / agent-platform engineering signals (current Sep 2026). Market refresh: Python/TypeScript full-stack delivery; Agents & Conversations; Studio connectors + MCP; durable Workflows; Search Toolkit + OCR4; Vibe/Devstral engineering; Forge/custom models; private deployment; evals, observability, security and adoption/ROI.
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Build production Mistral applications with disciplined full-stack and AI-assisted engineering
Agents, conversations + full-stack application engineering
Coverage: Mistral API / Agents & Conversations / Python / FastAPI / TypeScript / React / Structured Outputs
Vibe/Devstral coding workflow + software quality
Coverage: Mistral Vibe / Devstral / Repo Context / Subagents / Testing / Git / CI-CD / Human Review
Ground agents in enterprise data and compose durable workflows with governed tools
Enterprise retrieval, OCR + production search
Coverage: Search Toolkit / OCR4 / RAG / Document AI / Citations / Multimodal Retrieval / Production Indexing
MCP connectors, Workflows + human-approved actions
Coverage: MCP / Connectors / Tool Calling / Durable Workflows / Handoffs / Human Approval / Telemetry
Customize and operate secure Mistral systems across private and production environments
Custom models, sovereignty + private deployment
Coverage: Forge / Fine-Tuning / Open Weights / VPC / On-Prem / Air-Gapped / Data Governance / Model Selection
Evals, observability + production reliability
Coverage: Evaluation / Tracing / SLOs / Latency-Cost / Fallbacks / Security / Rollback / Adoption & ROI
Role-Readiness Proof / Capstone
Build a production Mistral enterprise agent for a consequential cross-system workflow. Ship a typed Python/FastAPI + TypeScript/React application using Agents/Conversations; ground it with OCR4/Search Toolkit over enterprise documents; add at least two governed connectors or MCP tools and one durable Workflow with explicit human approval; use Vibe/Devstral for a verified code change; select or customize the model where useful; deploy through an appropriate cloud/VPC/on-prem path; instrument evals, traces, latency, cost, permissions and failure recovery; then run a staged rollout/rollback and present architecture, runbook, adoption and ROI evidence.
Proof Rubric
- Demonstrate sound model/context choices
- maintainable full-stack code and tests
- grounded retrieval/OCR quality
- correct connector, MCP and workflow contracts
- secure permissions and approvals
- disciplined AI-assisted coding with human verification
- useful customization or model-selection judgment
- eval-driven regression gates
- observable latency/cost/reliability
- safe recovery from partial failures
- deployment discipline
- measurable adoption/business impact
- and evidence that production feedback changed the design