AI Engineer (LLMs & AI Agents)

Project description

VR-122545

Join our Development Centre in Bucharest and become a member of our open-minded, progressive and professional team. In this role you will be working for one of our world-famous clients.

The Chief Security Office (CSO) of our client comprises the Chief Information Security Office (CISO) and the Corporate Security unit. The CISO organization guarantees information security for our client.

The role resides inside the Cyber Hygiene Engineering team within CSO. The team is responsible for developing and maintaining security solutions that improve the organization's security posture through automation, cloud security, vulnerability management, and application security.

We are looking for a Python-focused AI and data science engineer to help design, build, and improve practical AI automation workflows. This role is suited for someone who can combine strong software engineering discipline with applied machine learning, data analysis, and LLM-based tooling.

On top of attractive salary and benefits package, Luxoft will invest into your professional training, and allow you to grow your professional career.

Responsibilities

• Design, build, and maintain Python-based AI workflows, evaluation harnesses, and automation tools.
• Work with LLMs, AI agents, prompt pipelines, embeddings, and retrieval-augmented generation.
• Analyze structured and unstructured data to support automation, decision-making, and model evaluation.
• Create datasets, validation sets, benchmarks, and evaluation metrics for AI-assisted workflows.
• Integrate AI tools with existing business processes, internal data sources, and developer workflows.
• Improve reliability, observability, and repeatability of AI outputs.
• Document technical decisions, workflows, and assumptions clearly.

Skills

• Strong Python experience, including clean scripting, packaging, testing, and data processing.
• Practical experience using LLM APIs, AI agents, prompt engineering, or AI automation frameworks.
• Ability to design experiments, evaluate model outputs, and interpret results.
• Familiarity with notebooks, Git, virtual environments, and reproducible development workflows.
• Good software engineering habits, including tests, readable code, version control, documentation, and maintainability.
• Ability to turn ambiguous business or workflow problems into concrete technical prototypes.