GenAI and LLM Operations Engineer

What you’ll gain

• Hands-on experience building production-grade AI/LLM systems
• Opportunity to work on real business use cases (not just prototypes)
• Collaboration with international teams
• Influence on how AI solutions are designed and scaled within the organization
• Space to experiment and grow in a rapidly evolving field

Why join?

This role is for engineers who see AI as the next evolution of data and software systems — and want to help shape how it’s built and applied in practice.

What we’re looking for

Core requirements:

• Strong Python skills and experience building production systems
• Experience with:
  • data pipelines
  • relational and/or NoSQL databases
  • APIs and system integrations
• Hands-on experience with:
  • CI/CD and version control (e.g. GitHub)
  • cloud platforms (AWS, Azure, or GCP)
• Understanding of production systems (monitoring, debugging, reliability)

AI / LLM (not required at expert level)

• Interest in Generative AI and LLMs
• Initial hands-on experience (projects, experiments, or courses)
• Basic understanding of:
  • prompt engineering
  • RAG / semantic search
  • LLM capabilities and limitations

Who will thrive in this role?

• Self-starters who can work independently and take ownership
• Engineers who learn quickly and adapt to new technologies
• People who value iteration and delivery over perfection
• Candidates comfortable working across engineering, data, and business domains

⭐ Nice to have

• Background in data engineering or data platforms
• Experience with tools like Airflow, dbt, Spark, Snowflake, etc.
• Exposure to AI/ML or LLM-based applications
• Experience in large organizations or regulated environments

Responsibilities

You’ll be part of a global team (Europe + US) working on solutions within Crop Science (agriculture innovation). The role is based in Europe, with standard working hours aligned to the local time zone.

What you’ll do

You will design and build solutions that:

• leverage LLMs and agent-based systems to automate complex workflows
• support the software development lifecycle (SDLC) using AI
• enable conversational access to data and insights
• integrate AI capabilities into existing engineering and data ecosystems

⚙️ Key responsibilities

AI systems & automation

• Design and develop LLM-powered and multi-agent solutions
• Build workflows where AI supports tasks such as requirements analysis, task creation, and code generation

Engineering & integration

• Integrate AI solutions with engineering tools (e.g. code repositories, CI/CD pipelines)
• Develop scalable, reliable system components

Data & interfaces

• Build natural language interfaces for querying complex data
• Combine structured and unstructured data sources
• Ensure accuracy, explainability, and control in AI-driven outputs