Key Responsibilities:

- Design, build, and operate production AI features: RAG pipelines, LLM-driven recommendations, conversational agents, or agentic workflow automation.

- Build the shared AI platform layer: retrieval infrastructure, eval frameworks, model monitoring, guardrails, and observability.

- Write LLM applications and integrations with marketing platforms, BI tools, or customer-facing product surfaces.

- Evaluate model and feature quality using structured eval frameworks; iterate on prompts, retrieval strategies, and model selection using data.

- Use AI-driven SDLC tooling such as Claude Code as a daily practice for both AI and non-AI code.

- Coordinate with the Personalization team to align GenAI product features with existing ML personalization signals.

- Document AI system design decisions, evaluation results, and operational lessons in the shared knowledge base.

Requirements:

- Bachelor’s degree in Computer Science or equivalent professional experience

- 7+ years of professional software development experience

- 3+ years of professional AI engineering experience

- Python proficiency

; comfortable building and operating production LLM applications.

- Hands-on experience with at least one specialization: RAG and retrieval systems, LLM evaluation, agentic frameworks (LangChain, LlamaIndex, or similar), or LLM-based workflow automation.

- Understanding of prompt engineering, context window management,



and LLM output quality tradeoffs.

- Familiarity with vector databases, embedding models, or semantic search.

- AI-driven SDLC (required):

hands-on experience shipping production code with AI-assisted development tools such as Claude Code, GitHub Copilot, or Cursor. The bar is not awareness; it is daily use in delivering real software.

- Familiarity with one or more:

.NET/C#, Go, Python, Java, React, MS SQL Server, Azure or AWS.

- Full-stack awareness:

comfortable contributing across layers of the stack when needed; purely single-layer specialists are not the target profile.

- Production ownership:

experience owning features end-to-end from spec through deployment and ongoing operations.

- Code quality fundamentals:

strong grasp of software design principles, automated testing, code review, and CI/CD.

- D3 (7+ yrs)

: independently delivers features with some guidance; strong fundamentals; beginning to make broader technical contributions.

- D4 (10+ yrs)

: fully autonomous; drives technical decisions within the team; mentors junior engineers.

Nice to Haves:

- Experience with e-commerce platforms, product catalogs, or high-traffic consumer applications.

- Exposure to MLOps tooling or model deployment pipelines.

- Experience working in distributed teams across the US, China, and Latin America.

📌 AI Engineer (Ciudad de México)
🏢 The Functionary
📍 Ciudad de México