Assignment Description

We are seeking a Senior AI Solutions Engineer to design and implement end-to-end AI solutions that enhance existing products and operational workflows. The role follows a hybrid model with on-site presence required three days per week.

You will develop new AI-driven capabilities and improve existing systems, focusing on delivering measurable improvements in production environments. The work covers the full lifecycle, from problem definition and model design to deployment of usable solutions, using large language models alongside traditional machine learning techniques.

As part of a small, specialized AI team, you will take strong ownership of your work. Responsibilities include turning unclear or evolving requirements into practical technical solutions such as models, data pipelines, and decision systems. You will integrate AI capabilities with data, tools, and business processes, and build services (e.g., APIs) to make them accessible in applications. Ongoing evaluation and refinement based on real-world usage, constraints, and trade-offs is expected. Close collaboration with Product Owners and engineers is required to deliver working solutions, with shared responsibility across the full DevOps and MLOps lifecycle.

Mandatory Qualifications

• A minimum of five years of experience in software development or a similar position
• Ability to independently architect and deliver complete end-to-end solutions, with clear ownership of quality outcomes
• Experience using machine learning approaches such as regression, classification, and time-series methods
• Strong analytical and problem-solving skills with a practical, engineering-focused mindset
• At least one year of experience working with AI solutions in production environments
• High level of competence in Python, with the capability to create and deploy simple services (e.g., FastAPI)
• Ability to make LLM-based systems dependable in real-world scenarios, taking into account evaluation practices, error handling, latency, and cost considerations
• Extensive experience working at a senior level in applied AI and machine learning engineering
• Hands-on background in developing and operating LLM-based solutions in production, including tool usage and multi-step processing flows
• Comfortable working collaboratively and sharing responsibility for the full DevOps and MLOps lifecycle

It is considered an advantage if you have experience with containerization technologies such as Docker and familiarity with cloud platforms, particularly Azure. It is also beneficial to have worked with unstructured or complex real-world data, as well as having prior exposure to enterprise-level systems or operational workflows.