AI Solutions Engineer

# AI Solutions Engineer

We are seeking an AI Solutions Engineer who bridges the gap between business opportunities and technical implementation. In this role, you will design, build, and operationalize AI-driven solutions that become part of our core platforms.

## About your role

As an AI Solutions Engineer, you will play a central role in bringing AI capabilities to production at Keylane. Your work helps identify where AI can have the greatest impact for our customers and the company.

You will serve as Keylane's AI specialist in customer conversations, help customers explore AI opportunities in their business processes, and translate their needs into practical solutions that can be embedded in our software platforms. Working alongside product teams, software engineers, architects, and other stakeholders, you will turn ideas into scalable AI capabilities that deliver measurable value.

From intelligent assistants and retrieval-based solutions to more advanced agentic workflows: you will design, build, and operationalize AI-driven functionality in Keylane's products. Working within the Microsoft Azure ecosystem, you will help establish practices needed to make AI work reliably and responsibly, including evaluation, monitoring, testing, and performance optimization.

As Keylane's data capabilities mature, you will also contribute to broader machine learning initiatives such as predictive modelling, anomaly detection, and intelligent document processing.

## What you bring

• At least 4 years of experience in Applied AI, Machine Learning, Data Science, or a related field, including hands-on experience delivering AI-driven solutions in production environments
• Strong Python skills
• Experience with modern AI frameworks, building AI-driven applications and agents, and applying model fine-tuning techniques such as instruction tuning, LoRA, or QLoRA
• Experience operationalizing AI solutions, including prompt management, evaluation, monitoring, testing, and continuous improvement in production environments
• Thorough understanding of machine learning fundamentals, including model evaluation, feature engineering, and statistical concepts
• Experience with data, including SQL and data preparation techniques
• Excellent written and verbal communication skills, with the ability to collaborate effectively with technical and non-technical stakeholders
• Confidence to present ideas