client is one of the world's largest e-commerce retailers of health, wellness, and beauty products, serving customers in more than 185 countries. With a catalog of over 30,000 products and integral logistics infrastructure, they help millions of people live healthier lives every day. They are growing its engineering organization around an ai-first mandate: using ai not just as a product feature but as the foundation for how they build software, serve customers, and operate the business.

job description

they are looking for a sr. Software ai engineer who will write production code, own features end-to-end from spec through deployment, operations, and observability, and participate in on-call rotation for what the team ships. There is no handoff to a separate ops or reliability function; engineers own the full lifecycle. Test automation is built into the shipping process using ai-driven tooling and the golden path.

build the genai-powered product experiences and the shared ai platform infrastructure that powers them. This includes rag pipelines over the catalog and customer reviews, llm-driven personalization, a conversational wellness agent, agentic workflow systems, and the evals and mlops layer that makes ai features production-grade and repeatable. Specializations within this track include: rag and personalization, agent framework and tool use, evals and guardrails, and llm application development for internal business functions such as marketing automation and bi agents.

what you will do

- 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.
- own the observability of ai systems you build: latency, cost, quality drift, and error rates; participate in on-call rotation and respond to production incidents.

qualifications

- 8+ years of software engineering experience. Fully autonomous; drives technical decisions within the team; mentors junior engineers.
- 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 : 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.
- 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,

nice to have

- exposure to mlops tooling or model deployment pipelines.
- contributions to internal developer tooling, golden path standards, or sdlc process improvements.
- 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 different time zones / geographies.
- track record of documenting architectural decisions, writing rfcs, or contributing to engineering wikis.

additional information

- selected candidates will be invited to take part in several rounds of interviews.
- the role is expected to be full time and ideal candidates should be looking for a long term engagement.
- a background check will be required as part of the onboarding process.

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📌 Sr software ai engineer (México)
🏢 Upwork
📍 México