Mid AI Engineer (k/m)

Nest Bank is a banking institution recognized for numerous practical AI implementations in business processes and a leader in applying artificial intelligence in the banking sector. The AI and Data Engineering Department creates modern service ecosystems based on ML models and generative AI solutions. The team works daily with Python, Azure services (including Azure OpenAI and Azure AI Search), AWS, and Databricks to develop AI systems improving natural language understanding, assisting advisors, automating processes, and supporting better decisions.

• Commercial experience as a Python Developer or AI Engineer
• Practical experience with public cloud platforms Azure and/or AWS
• Strong knowledge of Python and software engineering best practices
• Experience building and integrating APIs
• Practical experience working with LLM models, building RAG-based solutions, and evaluating response quality
• Familiarity with machine learning or NLP libraries and frameworks
• Practical experience with data handling, preparation, and SQL
• Knowledge of CI/CD tools and patterns; experience with containerization and deployment automation
• Commitment to technical and domain growth in AI and desire to impact AI usage in banking

Nice to have:

• Experience defining and maintaining cloud infrastructure using Terraform
• Experience with speech recognition (speech to text) and speech synthesis (text to speech) models

• Design and implement microservices and services using machine learning and generative AI models
• Integrate AI solutions with banking systems and customer channels
• Develop and maintain backend services and APIs supporting AI usage in the bank
• Create and maintain CI/CD pipelines for MLOps in public cloud environments
• Collaborate with Data Science and Data Analytics units on data preparation, experiments, model quality assessment, and production deployment
• Optimize performance, quality, and cost of AI models and services
• Design solutions considering security, regulatory compliance, and monitoring
• Document solutions and share knowledge and best practices within the team
• Design and develop conversational solutions using speech recognition (speech to text) and speech synthesis (text to speech) models