# AI Solutions Engineer

**Company:** [Pinterest](https://hotfix.jobs/companies/pinterest)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $124k – $255k/yr
**Skills:** Python, Agentic Frameworks, LangGraph, OpenAI, Anthropic, Vertex Ai, Bedrock, Mcp, RAG, Vector Databases, CI/CD, LLMs
**Posted:** 2026-03-17

> Partners with business teams to identify AI automation opportunities, designs and ships production AI solutions using agentic frameworks and Python, ensures responsible AI practices, and drives adoption. Requires software engineering foundation, hands-on AI delivery, and business acumen.

## Job Description

## What you'll do:
- **Discover and scope AI opportunities**: Partner with internal teams to understand workflows, identify bottlenecks, and propose AI-enabled changes for business outcomes.
- **Design end-to-end AI solutions**: Implement intuitive AI tools integrating with existing systems and data sources.
- **Build and ship production-quality software**: Write clean code, use CI/CD, implement logging, monitoring, and guardrails.
- **Pilot, rollout, and drive adoption**: Gather feedback and iterate based on usage.
- **Champion responsible AI**: Ensure privacy, security, and compliance.
- **Build for reuse**: Create reusable patterns and documentation.

## What we're looking for:
- Software engineering foundation with CS/Engineering/Data Science degree or equivalent, experience building production systems.
- Hands-on AI/automation delivery, e.g., workflow automation or AI service integrations (OpenAI, Anthropic, Vertex AI, Bedrock).
- Agentic AI literacy: understanding of local/remote agents, MCP, Agent Skills/Hooks, A2A coordination.
- System design skills: data flows, integrations, trade-offs, failure handling.
- Data/security judgment: access controls, PII minimization, audit logging.
- Business acumen for corporate functions and clear communication.

**Preferred Qualifications**:
- Experience with corporate functions.
- Agentic frameworks (LangGraph, Claude Agent SDK).
- MCP server design.
- AI evaluation at scale (eval sets, A/B testing).
- Responsible AI frameworks, RAG pipelines, vector databases.
- CI/CD for AI (prompt versioning, model pinning).

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