Ai&machine learning engineer

We are looking for a AI&ML Engineer to join our team and help design, build, deploy, and optimize machine learning solutions at scale.

In this role, you will be responsible for developing both classical machine learning models and Generative AI applications, with a strong focus on production-grade systems. You will work across forecasting, anomaly detection, LLM-powered solutions, and agentic workflows, leveraging modern cloud and MLOps practices on Azure and Databricks.

The role combines machine learning engineering, software development, cloud infrastructure, and Generative AI, with a strong focus on delivering reliable, scalable, and measurable business outcomes.

Responsibilities

Classical Machine Learning & Production Systems

• Design, develop, and deploy forecasting and time-series prediction models in production environments
• Build and maintain scalable ML pipelines using Databricks, MLflow, and model-serving capabilities
• Develop feature engineering workflows and manage experiment tracking and model versioning
• Implement model monitoring, drift detection, and automated retraining strategies
• Ensure reproducibility, reliability, and scalability of machine learning solutions
• Collaborate with cross-functional teams to integrate ML models into business applications and services

Generative AI & Agentic Systems

• Develop LLM-powered applications using prompt engineering, function calling, and retrieval techniques
• Design and implement multi-step agentic workflows using orchestration frameworks such as LangGraph, CrewAI, or similar technologies
• Build evaluation frameworks to assess LLM output quality, performance, and agent behavior in production
• Create and maintain datasets for model fine-tuning, testing, and evaluation
• Optimize inference performance, cost, and response quality for Generative AI systems

Infrastructure & Deployment

• Deploy machine learning solutions using Azure Databricks and Azure Machine Learning
• Containerize applications using Docker and orchestrate workloads with Kubernetes
• Implement observability, monitoring, and tracing capabilities for ML models and AI agents
• Support CI/CD and MLOps practices across the machine learning lifecycle
• Contribute to the design of scalable and resilient cloud-native ML architectures

Requirements

• 4–5 years of professional experience in Machine Learning, AI Engineering, or related roles
• Strong Python programming skills and solid object-oriented programming fundamentals
• Hands-on experience building and deploying forecasting or time-series prediction models
• Experience working with production-grade ML workflows and Databricks environments
• Solid understanding of Large Language Models (LLMs), prompt engineering, and agentic system design
• Experience with Azure cloud services and modern ML deployment practices
• Proficiency with Docker and Kubernetes for application deployment and orchestration
• Knowledge of model evaluation, monitoring, drift detection, and performance optimization techniques
• Experience with workflow orchestration tools such as Apache Airflow or similar platforms
• Strong understanding of software engineering best practices, testing, and maintainable code development
• Strong analytical and problem-solving skills
• Ability to work effectively in international and multidisciplinary teams
• Strong communication and collaboration skills
• Fluency in English

Nice-to-have

• Experience deploying LLM applications and agentic systems in production environments
• Knowledge of orchestration frameworks such as LangGraph, CrewAI, OpenAI Agent SDK, or similar technologies
• Familiarity with observability and tracing platforms for LLM applications (e.g., Langfuse)
• Understanding of inference optimization, token caching, and cost-control strategies for Generative AI workloads
• Experience with multi-agent systems and advanced tool-use patterns

If this sounds like you, share your CV with us and let's talk!