Machine Learning Engineer

• High Technical Impact: A unique professional challenge to build and operate production ML systems and agentic workflows for a stable, market-leading global enterprise.

• Competitive Compensation: Competitive base salary and annual target bonus.

• Benefits Package: Cafeteria allowance and private health insurance package.

• Flexibility: Hybrid working model requiring 3 days of office presence (Budapest) and offering 2 days of Home Office per week.

Responsibilities

• ML Pipeline Engineering: Build and maintain production ML pipelines - feature engineering, model training, evaluation, deployment, and monitoring on AWS and Databricks.

• Agentic AI Workflows: Implement and operate agentic AI workflows using frameworks such as LangChain, LangGraph, or equivalent, following architectural patterns established by senior engineers while contributing improvements based on production observations.

• Feature Engineering Pipelines: Develop and maintain feature engineering pipelines, ensuring data quality, freshness, and consistency across batch and real-time serving paths.

• MLOps Infrastructure: Operate and extend MLOps infrastructure: model CI/CD, experiment tracking, automated retraining, model versioning, and performance monitoring using MLflow and Databricks tooling.

• LLM Applications: Build and maintain RAG pipelines, vector database integrations, prompt management systems, and tool-use components for LLM-powered applications.

• Monitoring & Alerting: Implement model monitoring dashboards and alerting: tracking drift, latency, error rates, and cost, escalating anomalies with context.

• Code Quality & Production Operations: 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.

• Cross-Team Collaboration: Collaborate with data engineers on upstream pipeline dependencies and with data scientists on translating research outputs into deployable services.

Requirements

Required Experience

• Industry Experience: 3+ years in machine learning engineering, applied ML, or software engineering with a meaningful ML focus and recent production delivery.

• Production Deployment: Production experience deploying and maintaining ML models in a serving environment handling real inference traffic beyond the notebook stage.

• Databricks & AWS Ecosystem: 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).

• LLMs & Agentic AI: 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.

• Software Engineering & Core ML: 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.

• Deployment & Infrastructure: Familiarity with containerized deployment (Docker, ECS, or Kubernetes) and infrastructure-as-code concepts.

Preferred Qualifications

• Experience with real-time inference serving and performance tuning: latency profiling, model optimization, caching, and scaling.

• Hands-on experience with evaluation frameworks for agentic systems: task success measurement, hallucination detection, and cost tracking.

• Familiarity with streaming data systems (Kafka, Kinesis, Spark Structured Streaming) as they relate to real-time feature computation and online inference.

• Experience with fine-tuning LLMs or training custom models on domain-specific data.

• Exposure to data governance practices: lineage tracking, access controls, and model documentation within Unity Catalog or equivalent.

• BS or MS in Computer Science, Machine Learning, or a quantitative field (practical production experience weighted equally).