Regular AI Engineer (Python)

We are looking for an experienced AI Engineer with strong Python skills (min. 3 years) and at least 1 year of hands-on experience building and deploying LLM-based applications (or AI solutions used in production projects). The role focuses on delivering AI services that automate business processes and on building robust agent-based solutions, including RAG and multi-step agentic workflows.

Responsibilities:

• Build AI services and applications in Python to support process automation.

• Develop LLM-based bots and assistants, including RAG solutions and multi-step task agents.

• Implement agentic workflows: define agent roles, integrate tools, control execution flow, and handle failures.

• Work with internal AI frameworks (extend and maintain them) and develop new components/frameworks when needed.

• Support business teams during application testing: troubleshoot issues, identify root causes, and propose fixes.

• Ensure consistency, maintainability, and high quality of delivered solutions (standards, reviews, best practices).

• Collaborate with other platform teams and with business teams consuming platform capabilities.

• Provide technical guidance to teams using frameworks developed by the platform group.

Must-have's:

• Very good knowledge of Python (min. 3 years).

• Practical experience with LangChain and LangGraph.

• Strong understanding and hands-on use of:

  • RAG

  • Embeddings / Vector Search

  • LLM Agents / Agentic Workflows

  • Tool Calling

• At least 1 year of practical experience delivering LLM-based solutions (including RAG and/or agents) in real projects.

• Strong Git skills, including advanced history/branch management (rebase, cherry-pick, revert, squash, rewriting commit order/messages).

• Experience working with Jira, Confluence, Bitbucket.

Nice to have's:

• Azure AI stack knowledge: AI Foundry / Azure OpenAI / Azure AI Search.

• Experience with Databricks, Cosmos DB, Redis, Grafana.

• Basic UI skills using React or Streamlit (e.g., internal demos/tools).

• Understanding of ML basics and topics like LLM fine-tuning.

• Ability to design system architecture and produce High-Level Design documentation.