Software Engineer
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Summary
Forward-deployed AI engineer (remote, US) embedded in customer environments to scope AI use cases, build production LLM applications — RAG pipelines, multi-step agents, MCP tool integrations — in Python, connect models to customer systems, and measure accuracy and cost in production.
About the Company
Forward deployed engineer, AI (remote)Our client puts engineers directly inside customer environments to build AI systems that work on messy, real data.
About the Role
You'll sit between the customer and the product team: scoping the problem, writing production code in Python, and getting it live. Remote, US-based.
Responsibilities
- Work with customer engineering and business teams to scope AI use cases and decide what's worth building.
- Build and deploy LLM applications: RAG pipelines, multi-step agents, and tool integrations over MCP.
- Connect models to the customer's systems: APIs, databases, data warehouses, SSO.
- Write evals, measure accuracy and cost, and fix what breaks in production.
- Bring what you learn in the field back to the product team so the next deployment goes faster.
Qualifications
- 8+ years in software engineering, with recent work shipping LLM-powered systems to production.
Required Skills
- Strong Python, plus TypeScript or another backend language.
- Hands-on work with LLM APIs from OpenAI, Anthropic (Claude), or Google (Gemini).
- Experience with LangChain, LangGraph, LlamaIndex, Model Context Protocol (MCP), or the OpenAI Agents SDK.
- Vector search with pgvector, Pinecone, or Weaviate.
- Cloud deployment on AWS, Azure, or GCP, including Bedrock, Azure OpenAI Service, or Vertex AI.
- You're comfortable in front of a customer's VP in the morning and in their codebase that afternoon.
Preferred Skills
- Daily use of Claude Code or Cursor.
- Agent-to-agent work using Google's A2A protocol.
- Prior consulting, solutions engineering, or customer-facing engineering roles.
- Experience with HIPAA or PII-regulated data.