Machine Learning Engineer
Via agency · Randstadhu
Software developerApplications developerSystems analystFlexibleFull-timeBudapest
Our client is a US-headquartered, market-leading enterprise in their sector with a multi-decade history and an agile, global network. We are seeking highly skilled Machine Learning Engineers to join their expanding Budapest team as technical individual contributors. Working within the Data Engineering organization alongside senior ML engineers, data engineers, and data scientists, you will focus on implementing, deploying, and maintaining ML pipelines and AI-powered features with production-level rigor.
Tasks
- Build and maintain production ML pipelines — feature engineering, model training, evaluation, deployment, and monitoring on AWS and Databricks.
- Implement and operate agentic AI workflows using frameworks such as LangChain, LangGraph, or equivalent.
- Develop and maintain feature engineering pipelines, ensuring data quality, freshness, and consistency across batch and real-time serving paths.
- Operate and extend MLOps infrastructure: model CI/CD, experiment tracking, automated retraining, model versioning, and performance monitoring using MLflow and Databricks tooling.
- Build and maintain RAG pipelines, vector database integrations, prompt management systems, and tool-use components for LLM-powered applications.
- Implement model monitoring dashboards and alerting: tracking drift, latency, error rates, and cost, escalating anomalies with context.
- Write production-quality Python code with thorough testing, documentation, and adherence to team engineering standards.
- Participate in on-call rotations for ML systems, triaging production issues and implementing fixes with appropriate urgency.
- Collaborate with data engineers on upstream pipeline dependencies and with data scientists on translating research outputs into deployable services.
Conditions & benefits
Competitive CompensationCompetitive base salary and annual target bonus.Benefits PackageCafeteria allowance and private health insurance package.FlexibilityHybrid working model requiring 3 days of office presence (Budapest) and offering 2 days of Home Office per week.
Requirements
- 3 years in machine learning engineering, applied ML, or software engineering with a meaningful ML focus and recent production delivery.
- Production experience deploying and maintaining ML models in a serving environment handling real inference traffic beyond the notebook stage.
- Working proficiency with the Databricks ML ecosystem (MLflow, Model Serving, Feature Store) and familiarity with supporting AWS services (SageMaker, S3, Lambda, Step Functions, ECS/EKS).
- Hands-on experience building applications with LLMs: RAG implementations, vector databases, prompt engineering, and API-based model integration (OpenAI, Anthropic, Bedrock, or equivalent).
- Production exposure to agentic AI patterns: multi-step orchestration, tool use, or workflow automation using LangChain, LangGraph, or similar frameworks.
- Strong Python proficiency and solid software engineering fundamentals: version control, testing, CI/CD, code review, and debugging production systems.
- Experience with at least one core ML framework (PyTorch, TensorFlow, scikit-learn, XGBoost) applied to production prediction tasks.
- Familiarity with containerized deployment (Docker, ECS, or Kubernetes) and infrastructure-as-code concepts.