Python programmer állások, munkák programozó, fejlesztő kategóriában

Design, develop, and maintain AI agents for manufacturing use cases (e.g. quality inspection, anomaly detection, process optimisation), using machine learning, deep learning, GenAI, rules, and reasoning....

• Design, build, and ship production retrieval systems
• Own vector store selection and operation
• Build and operate indexing and refresh pipelines
• Implement semantic and hybrid retrieval
• Own retrieval evaluation as a first-class system
• Make and defend the core tradeoffs of the domain

Design, train, and evaluate machine learning models for manufacturing use cases such as quality prediction, anomaly detection, predictive maintenance, and process optimisation, covering the full ML lifecycle from problem definition to performance tuning....

• Design and build knowledge tracing and longitudinal learner models that support mastery and progression features surfaced to customers
• Define what "mastery" and "progression" mean operationally: constructs, model targets, and evaluation criteria aligned to learning outcomes and product requirements

• Build and maintain scalable cloud infrastructures using modern services and best practices
• Proactive detection, analysis, and fixing of vulnerabilities and performance bottlenecks in cloud environments

• Build and operate multi-cluster containerized environments across cloud platforms
• Create end-to-end CI/CD pipelines for cloud-native applications and machine learning workloads
• Lead cost-optimization and FinOps initiatives for GPU, storage and data-egress spend

• Design and implement modern data architectures and lakehouse solutions
• Provide technical leadership throughout the project lifecycle
• Collaborate with business stakeholders
• Create effort estimations and provide technical expertise during the pre-sales phase
• Mentor 3-6 person project teams

• End-to-end implementation of data pipelines and GenAI applications on Databricks
• Design and maintenance of CI/CD, model serving, monitoring, and evaluation frameworks for AI production reliability
• Tuning of complex Spark jobs and LLM inference patterns for efficiency and latency optimization

• Design, develop, and optimize scalable data pipelines for processing large-scale structured and unstructured data
• Ensure reliability, performance, and scalability of data systems
• Build solutions to handle real-time streaming data from multiple sources

• Leading end-to-end penetration testing engagements across web applications, APIs, cloud environments, Kubernetes, AWS, Azure, GCP, and internal infrastructure
• Simulating real-world attack scenarios to identify security risks before they become threats

• Development of software tools for analysis of measurement data from the photon diagnostic devices.
• Support data analysis with the objective of characterizing the X-ray beam and calculate SASE spectral information based on the acquired data.
• Support group with hardware and cable assembly.

• Leading a team of 2-3 AI and cloud engineers focused on fast prototyping of AI/ML solutions in AWS cloud
• Providing mentoring and technical support to junior and medium teammates
• Acting as a trusted advisor to clients

• Design and implement cloud security controls across AWS, Azure, and/or GCP
• Deploy and tune Cloud Security Posture Management and Cloud-Native Application Protection Platform tooling
• Build security into Infrastructure-as-Code and CI/CD pipelines

• Build Data Pipelines: Design, build, and maintain scalable data pipelines for ingesting, transforming, and serving financial and operational data.
• Work with ERP Data: Integrate and model data from ERP systems and finance tools, including accounting, controlling, procurement, revenue, and cost data.

• Further development of existing optimization components of the airline management system Netline/Crew or Netline Scheduling area
• Implementation and experimentation on new and efficient optimization approaches
• Recording customer requirements for the commissioning of existing optimization components

• Implementation and maintenance of large-scale data pipelines and the Azure/Databricks data platform
• Building robust, scalable pipelines using Azure Databricks (PySpark / SQL)
• Design and enforcement of data architecture standards including Delta Lake / Lakehouse architecture and data modeling

• You understand RABOT's departments, their core processes and our AI tooling (Claude, Claude Code, our data stack) well enough to run a process-discovery session on your own.
• You've shipped your first automation into production — something a team feels immediately.

• Build and run evaluation pipelines for LLM-based systems
• Analyze metrics, identify edge cases, and track quality
• Create and maintain Golden and Silver datasets
• Work with evaluation frameworks and tools (like Langfuse)
• Develop Python scripts for AI workflows (APIs, data processing, monitoring)

• Implementing and evaluating state-of-the-art quantum algorithms (e.g., QAOA, VQE, quantum annealing or others) for an optimization problem in the scope of optical systems
• Implement prototypes and demonstrators to evaluate quantum-enhanced optimization concepts for ZEISS applications

As a Machine Learning Engineer in our Retail ML team, you will take end‑to‑end ownership of designing, developing and maintaining advanced machine learning solutions that directly influence our commercial performance. Your work will span dynamic pricing, demand forecasting and data‑driven buying...