ML Engineer

PLACE OF WORK 1112 Budapest, Boldizsár utca 2. AREA OF EMPLOYMENT IT START OF WORK as soon as possible EMPLOYMENT TYPE Full-time megállapodás szerint My responsibilities: Collaborates with Data Scientists to validate and scale new algorithms, spanning classical machine learning, deep learning, and LLM-based approaches, through pilot phases, and later industrializes these solutions at scale. Designs, builds and operates Generative AI / LLM applications (e.g., RAG pipelines, fine-tuned models, agentic workflows), applying LLMOps practices such as prompt and version management, systematic evaluation, guardrails, and cost/latency optimization Influences, contributes and maintains the large-scale data infrastructure required for the AI projects in close collaboration with the data engineers Leverages an understanding of software architecture and software design patterns to write scalable, maintainable, well-designed and future-proof code Designs, develops and maintains the framework for analytical and ML pipelines, applying MLOps practices: CI/CD for models, automated training and retraining workflows, model registry and reproducible deployments Develops common components to address pain points in machine learning projects, such as model lifecycle management, feature stores, data quality evaluation, and evaluation harnesses for LLM-based applications Implements monitoring and observability for models in production, including tracking data and model drift, performance degradation, and, for GenAI applications, output quality and hallucination. Supports responsible AI practices: contributes to model risk management, explainability, and compliance with applicable AI regulation (e.g., EU AI Act) and internal governance standards Provides input and helps implement frameworks and tools to improve data quality Works in cross-functional agile teams of highly skilled software/machine learning engineers, data scientists, designers, product managers and others to build the AI ecosystem within the Group Delivers on time, demonstrating strong commitment to deliver on the team mission and agreed backlog The knowledge I own: Has a background in computer science, mathematics or related technical discipline Is experienced in software engineering with exposure to statistical, data science and AI/ML roles Has deep knowledge and proven experience with optimizing and operating machine learning models in a production context; hands-on experience deploying and operating LLM-based applications is a strong plus Worked with Python in a productive environment (mandatory). Background in programming in C, C++, Java and Scala is beneficial. Exposure to both streaming and batch analytics. Experience with the core data/ML stack is beneficial: SQL, Spark, Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, Databricks Experience with MLOps tooling is beneficial: MLflow or comparable experiment tracking and model registry, workflow orchestration (e.g., Azure Data Factory, Databricks Declarative Automation Bundles), containerization and orchestration (Docker, Kubernetes), CI/CD, infrastructure-as-code, and cloud ML platforms Experience with the GenAI/LLM ecosystem is beneficial: Hugging Face, LLM APIs, orchestration frameworks (e.g., LangChain, LlamaIndex), vector databases (e.g., pgvector, Azure/Databricks AI Search), and LLM evaluation/observability tooling Has experience working with large data sets, simulation/optimization and distributed computing tools (e.g., Spark, Ray) Agile / Digital Experience Has experience working in AI startup environment or organizations with an agile culture Has a professional attitude and service orientation; superb team player Individual Skills Has sound problem-solving skills with the ability to quickly process complex information and present it clearly and simply Demonstrates good written and verbal communication skills along with strong desire to work in cross-functional teams Mindset & Behaviors Is able to build a sense of trust and rapport in terms of quality Has an attitude to thrive in a fun, fast-paced, startup-like environment Is open minded to new approaches and learning The offer that would convince me: We develop and maintain our own product with high emphasis on quality and long-term stability Code quality matters: we follow Clean Code and SOLID principles backed by robust testing Flexible working hours and remote work options Competitive salary with regular adjustments based on inflation and loyalty Access to continuous learning via our internal learning platform and expert communities Online application: Please use our online application and attach your resume. AIIS Adatkezelési tájékoztató