Job Description

Principal MLOps Engineer

Location: Remote from Spain (Spanish employment contract)

We are looking for Principal MLOps Engineer to support the development of AI-driven solutions in Global Digital Marketing team of a Leading Agricultural Company. The engineers will work on projects involving generative AI, intelligent agents, and process automation, integrating with large-scale enterprise systems. The current goals are to create two LLM agents, that are responsible for producing marketing Campaigns materials – campaign information, images, presentations, slogans, etc.

Requirements:

  • Proven experience in developing and deploying generative AI or LLM-based applications
  • Participate on the architectural design of conversational solutions.
  • Strong prompt proficiency with LLMs such as OpenAI, Azure OpenAI, Claude, or LLaMA
  • Hands-on experience with LangChain, LangGraph. Other frameworks are also valued.
  • Solid understanding of semantic search, RAG pipelines, and vector embeddings
  • AWS experience with ECR, ECS/Fargate and dockerization.
  • Design/Deploy MCP servers.
  • Background in API/microservices development
  • Ability to work with enterprise-grade architectures and process automation tools
  • English: at least B2 level.

Will be a plus:

  • Experience with n8n, Semantic Kernel, Copilot Studio,
  • Experience with MLOps, model optimization, and cloud deployments (especially AWS)
  • Previous experience with digital marketing life cycle.
  • SQL proficiency
  • Expertise using Claude Code, GitHub copilot and similar tools as part of their daily tooling, working routines

Responsibilities:

  • Lead the development of LLM-based intelligent agents
  • Prepare system architecture, agree system design with stakeholders
  • Contextualization of the company data.
  • Implement Retrieval-Augmented Generation (RAG) systems
  • Integrate LLMs with varius different data sources
  • Leverage tools like LangChain, LangGraph, Semantic Kernel, and Copilot Studio to build semantic workflows
  • Automate internal processes using n8n
  • Collaborate with cross-functional teams including DevOps, backend, and business stakeholders
  • Contribute to best practices for AI deployment, prompt engineering, and model evaluation

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