About this role
<div id="profile" class="profile"> <div class="profile_list"> <ul class="profile_section"> <li id="profile_heading1" class="profile_item profile_heading">PLACE OF WORK</li> <li id="profile_field1" class="profile_item profile_content">1112 Budapest, Boldizsár utca 2.</li> </ul> <ul class="profile_section"> <li id="profile_heading2" class="profile_item profile_heading">AREA OF EMPLOYMENT</li> <li id="profile_field2" class="profile_item profile_content">IT</li> </ul> <ul class="profile_section"> <li id="profile_heading3" class="profile_item profile_heading">START OF WORK</li> <li id="profile_field3" class="profile_item profile_content">as soon as possible</li> </ul> <ul class="profile_section"> <li id="profile_heading4" class="profile_item profile_heading">EMPLOYMENT TYPE</li> <li id="profile_field4" class="profile_item profile_content">Full-time</li> </ul> <ul class="profile_section"> <li id="profile_heading5" class="profile_item profile_heading"></li> <li id="profile_field5" class="profile_item profile_content"></li> </ul> </div> </div> <div id="additional_fields" style="display:none"> <div id="start_date">megállapodás szerint</div> </div> <style type="text/css"> .profile_item { list-style-type: none !important; } .profile_heading { font-weight: bold; } .profile_content { padding-bottom: 0.5rem; } .profile_section:has(.profile_item:empty) { display: none; } </style><div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px"><b>My responsibilities:</b></H2> </div><div><ul> <li>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. </li> <li>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 </li> <li>Influences, contributes and maintains the large-scale data infrastructure required for the AI projects in close collaboration with the data engineers </li> <li>Leverages an understanding of software architecture and software design patterns to write scalable, maintainable, well-designed and future-proof code </li> <li>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 </li> <li>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 </li> <li>Implements monitoring and observability for models in production, including tracking data and model drift, performance degradation, and, for GenAI applications, output quality and hallucination. </li> <li>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 </li> <li>Provides input and helps implement frameworks and tools to improve data quality </li> <li>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 </li> <li>Delivers on time, demonstrating strong commitment to deliver on the team mission and agreed backlog</li> </ul></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px"><b>The knowledge I own:</b></H2> </div><div><ul> <li>Has a background in computer science, mathematics or related technical discipline </li> <li>Is experienced in software engineering with exposure to statistical, data science and AI/ML roles </li> <li>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 </li> <li>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 </li> <li>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 </li> <li>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 </li> <li>Has experience working with large data sets, simulation/optimization and distributed computing tools (e.g., Spark, Ray) </li> </ul> <p><strong>Agile / Digital Experience </strong></p> <ul> <li>Has experience working in AI startup environment or organizations with an agile culture </li> <li>Has a professional attitude and service orientation; superb team player </li> </ul> <p><strong>Individual Skills </strong></p> <ul> <li>Has sound problem-solving skills with the ability to quickly process complex information and present it clearly and simply </li> <li>Demonstrates good written and verbal communication skills along with strong desire to work in cross-functional teams </li> </ul> <p><strong>Mindset & Behaviors </strong></p> <ul> <li>Is able to build a sense of trust and rapport in terms of quality </li> <li>Has an attitude to thrive in a fun, fast-paced, startup-like environment </li> <li>Is open minded to new approaches and learning </li> </ul></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px"><b>The offer that would convince me:</b></H2> </div><div><ul> <li>We develop and maintain our own product with high emphasis on quality and long-term stability</li> <li>Code quality matters: we follow Clean Code and SOLID principles backed by robust testing</li> <li>Flexible working hours and remote work options</li> <li>Competitive salary with regular adjustments based on inflation and loyalty</li> <li>Access to continuous learning via our internal learning platform and expert communities</li> </ul></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px"><b>Online application:</b></H2> </div><div><p>Please use our online application and attach your resume.</p> <p><a title="https://karrier.aldi.hu/fileadmin/user_upload/hu/karrier_oldal/allasok_szakteruletenkent/aiis/aiis_allasra_jelentkezok_adatkezelesi_tajekoztato_2807931_3_.pdf" href="https://karrier.aldi.hu/fileadmin/user_upload/HU/Karrier_oldal/Allasok_szakteruletenkent/AIIS/AIIS_Allasra_jelentkezok_adatkezelesi_tajekoztato_2807931_3_.pdf" target="_blank" rel="noreferrer noopener" aria-label="Hivatkozás: AIIS Adatkezelési tájékoztató">AIIS Adatkezelési tájékoztató</a></p> <p><a title="https://karrier.aldi.hu/fileadmin/user_upload/hu/karrier_oldal/allasok_szakteruletenkent/aiis/aiis_allasra_jelentkezok_adatkezelesi_tajekoztato_2807931_3__eng.pdf" href="https://karrier.aldi.hu/fileadmin/user_upload/HU/Karrier_oldal/Allasok_szakteruletenkent/AIIS/AIIS_Allasra_jelentkezok_adatkezelesi_tajekoztato_2807931_3__eng.pdf" target="_blank" rel="noreferrer noopener" aria-label="Hivatkozás: Privacy notice">Privacy notice</a></p></div></div></div>