Machine Learning Engineer

Machine Learning Engineer

Our partner is a dynamically growing company with a strong international background, leveraging data-driven operations to support advanced analytics and AI/ML-based business processes. Working in a modern technology environment, the company places a strong emphasis on the development and operation of scalable data platforms that drive innovation and business value.

We are looking for a Machine Learning Engineer to join their expanding team.

What we're looking for

• 3+ years of experience in Machine Learning Engineering, Applied ML, or Software Engineering with recent production ML delivery.
• Production ML deployment experience , including operating, monitoring, and maintaining models serving real-world inference traffic.
• Strong knowledge of the Databricks ML ecosystem , including MLflow, Model Serving, and Feature Store.
• Experience with AWS services supporting ML workloads (e.g., SageMaker, S3, Lambda, Step Functions, ECS/EKS).
• Hands-on experience building Generative AI / LLM applications , including:
• Retrieval-Augmented Generation (RAG)
• Vector databases
• Prompt engineering
• Integration with OpenAI, Anthropic, Bedrock, or similar APIs
• Experience with Agentic AI frameworks and multi-step workflow orchestration using LangChain, LangGraph, or similar tools.
• Strong Python programming and software engineering fundamentals, including testing, CI/CD, version control, code reviews, and debugging.
• Experience with leading ML frameworks such as PyTorch, TensorFlow, scikit-learn, or XGBoost in production environments.
• Familiarity with containerization and deployment technologies , including Docker, Kubernetes, ECS, and infrastructure-as-code concepts.
• Professional working proficiency in English with strong communication skills

Your responsibilities

• Build and maintain end-to-end ML pipelines , including feature engineering, model training, evaluation, deployment, and monitoring using Databricks and AWS.
• Develop and operate Agentic AI solutions using frameworks such as LangChain and LangGraph, contributing to workflow automation and orchestration improvements.
• Design and maintain feature engineering pipelines , ensuring data quality, consistency, and availability across batch and real-time environments.
• Support and enhance MLOps practices , including CI/CD, experiment tracking, model versioning, automated retraining, and monitoring with MLflow and Databricks.
• Develop LLM-powered applications , including RAG pipelines, vector database integrations, prompt management, and tool-use capabilities.
• Monitor model performance by tracking drift, latency, errors, and costs, and proactively addressing production issues.
• Write high-quality production code with strong testing, documentation, and engineering standards.
• Provide operational support through incident response, troubleshooting, and participation in on-call rotations.
• Collaborate across teams , partnering with Data Engineers and Data Scientists to move ML solutions from research into production.

What our partner offers

Hybrid working model

Flexible working environment

International and innovative technology landscape

Opportunity to work on exciting and complex projects

Ready for your next challenge?

If you're passionate about building and scaling production machine learning systems, deploying AI solutions that deliver real-world impact, and working at the intersection of predictive models, Generative AI, and modern MLOps practices, we'd love to hear from you.