Senior Machine Learning Engineer
Via agency · Randstadhu
Software developerSystems analyst
MathematicianFlexibleFull-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 a highly skilled Senior Machine Learning Engineer to join their expanding Budapest team as a senior technical individual contributor. Situated within the Data Engineering organization, you will operate at the intersection of data engineering, applied ML, and software engineering to design, build, and deploy production-grade ML infrastructure and agentic AI systems that directly impact the business.
Tasks
- Design, build, and maintain production ML pipelines end-to-end — feature engineering, model training, evaluation, deployment, serving, and monitoring on AWS and Databricks.
- Architect and implement agentic AI orchestration systems using frameworks like LangChain, LangGraph, CrewAI, or custom layers, with production-grade reliability, observability, guardrails, planning loops, and memory management.
- Build and optimize feature stores, training data pipelines, and feature engineering workflows serving both batch and real-time inference workloads.
- Own MLOps infrastructure including CI/CD for models, automated retraining pipelines, A/B testing frameworks, model versioning, experiment tracking, and ML asset governance using MLflow, Unity Catalog, and Databricks Model Serving.
- Design and implement LLM integration patterns including RAG architectures, prompt management systems, tool-use frameworks, vector databases, and memory/state management.
- Develop model monitoring for drift detection, performance degradation alerts, cost tracking, and automated remediation. Establish evaluation frameworks for predictive models and agentic systems.
- Drive build-vs-buy and framework selection decisions backed by prototypes and benchmarks. Mentor ML engineers through technical design reviews, code reviews, and architectural guidance.
Conditions & benefits
Competitive CompensationSenior level 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
- 6 years in machine learning engineering, applied ML, or closely related software engineering roles with recent, demonstrated production delivery.
- 2 years building and operating production ML systems handling real traffic and business-critical decisions (not just notebooks or proof-of-concepts).
- Production experience with Databricks ML ecosystem (MLflow, Model Serving, Feature Store, Unity Catalog) and supporting AWS services (SageMaker, Bedrock, S3, Lambda, Step Functions, ECS/EKS).
- Hands-on experience building agentic AI systems (multi-step orchestration, tool use, planning loops, memory management, human-in-the-loop patterns).
- Deep proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost) with strong software engineering fundamentals (testing, version control, code review, CI/CD).
- Production experience with LLM applications (RAG pipelines, vector databases like Pinecone/Weaviate/Chroma/pgvector, embeddings, prompt engineering, fine-tuning) and real-time/batch inference optimization (quantization, distillation, caching).