Senior Software AI Engineer – GenAI Platform Lead

Upwork; Ciudad de México; On-site; MXN 1,200,000 - 1,600,000; Full time

Job summary

Upwork is seeking a Sr. Software AI Engineer to build production-grade GenAI features and shared AI platform infrastructure. You will own end-to-end lifecycle from spec to deployment and on-call rotation, with AI-driven SDLC tools and strong emphasis on observability.

You will design and implement RAG pipelines, LLM-driven personalization, and agentic workflows, collaborating with the Personalization team to align AI features with ML signals.

Qualifications

• 8+ years of software engineering experience.
• Python proficiency; comfortable building and operating production LLM applications.
• Hands-on with RAG retrieval systems, LLM evaluation, or agent frameworks (LangChain, LlamaIndex).
• Understanding of prompt engineering, context window management, and LLM output quality tradeoffs.
• Familiarity with vector databases, embedding models, or semantic search.
• AI-driven SDLC: daily use of AI-assisted tools like Claude Code, GitHub Copilot, or Cursor.

Responsibilities

• Design, build, and operate production AI features (RAG pipelines, LLM-driven recommendations, conversational agents, workflow automation).
• Build the shared AI platform layer: retrieval infrastructure, eval frameworks, model monitoring, guardrails, observability.
• Write LLM applications and integrations with marketing platforms, BI tools, or customer-facing surfaces.
• Coordinate with personalization signals and document AI system design decisions, evaluation results, and operational lessons.
• Own observability of AI systems, latency, cost, quality drift, and participate in on-call rotations.

Job description

Upwork is seeking a Sr. Software AI Engineer to build production-grade GenAI features and shared AI platform infrastructure. You will own end-to-end lifecycle from spec to deployment and on-call rotation, with AI-driven SDLC tools and strong emphasis on observability.

You will design and implement RAG pipelines, LLM-driven personalization, and agentic workflows, collaborating with the Personalization team to align AI features with ML signals.