Machine Learning Engineer

Cégleírás / Organisation/Department 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.

Pozíció leírása / Job description 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. Elvárások / 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 infere