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AI prompt engineer
Thonglo
Full-time
Aperçu
We are looking for an AI Prompt Engineer who lives at the intersection of LLMs, automation, and real business use cases. Your mission will be to design, build, and optimize AI-powered workflows that automate critical parts of the sales funnel, with a strong focus on lead generation, enrichment, qualification, and outreach orchestration. You will work hands-on with n8n, Vertex AI / Gemini, RAG pipelines, and modern AI tooling to turn business logic into scalable, reliable AI systems used internally and by customers. This is a builder role, not a research role.
Responsabilités
- Design and implement AI-driven workflows using n8n for sales and lead-generation automation
- Build and maintain prompt systems optimized for Gemini and other LLMs
- Implement RAG pipelines (document ingestion, vector stores, retrieval logic, prompt injection)
- Connect LLMs to real-world data sources (CRM, LinkedIn data, APIs, internal knowledge bases)
- Develop internal AI use cases (sales ops, enrichment, scoring, personalization)
- Develop customer-facing AI features integrated into the product
- Continuously optimize prompts for accuracy, cost, latency, and reliability
- Work closely with product, engineering, and growth teams to translate business needs into AI systems
- Monitor, debug, and improve AI workflows in production
- Stay up-to-date with emerging AI standards and practices (MCP, new RAG patterns, fine-tuning methods)
Stack technique
- Strong hands-on experience with n8n (or similar workflow automation tools)
- Experience with Vertex AI and Gemini models
- Solid understanding of Prompt Engineering (system prompts, structured outputs, chaining, evaluation)
- Experience implementing RAG architectures
- Familiarity with vector databases and embeddings
- Understanding of fine-tuning concepts and when to use them vs RAG
- Ability to integrate APIs and external data sources
- Comfortable working with JSON, schemas, and structured outputs
Compétences humaines
- Strong problem-solving mindset
- Ability to translate vague business needs into concrete AI workflows
- High attention to detail and reliability
- Ownership mentality — you build it, you maintain it
- Clear communication with technical and non-technical stakeholders