Python programmer állások, munkák it programozás, fejlesztés területen

Act as a key expert for SAP S/4HANA data migration projects, with particular focus on Material Master and Production Master (I.e., BOMs, Work Centers, Routings and Production Versions)....

• 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, develop, and implement complex and scalable data solutions on the AWS cloud platform
• Design, build, and optimize data pipelines to ensure efficiency and reliability
• Write clean, efficient, and maintainable SQL and Python code
• Develop and maintain shell scripts to support automation

• Design of evaluation framework for scalable LLM-as-a-judge pipelines
• Development of rubrics and datasets including comprehensive grading rubrics and high-quality golden datasets
• Optimization of judge prompts for automated scoring alignment

• Support pipeline management and forecasting improvements for different business divisions using available data sources.
• Develop and maintain analytical tools and digital platforms to improve efficiency and self-service capabilities across the organisation.

Architect, build, and operate scalable inference services, APIs, and backend components for model-driven and LLM-powered product features...

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....

• Definition and improvement of metrics representing product performance and operations efficiency
• Data collection from multiple sources ensuring accuracy and completeness
• Data cleaning, organization, and transformation into usable formats for analysis

• 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

• Design and ship production-grade agentic systems
• Take technical ownership of Compliance in a Box (CiaB) AI platform
• Architect and evolve AI reasoning layer and agent orchestration framework
• Build and maintain the GRC Brain (AI reasoning layer) and Agentic Cortex (agent orchestration framework)

• Design, develop, and maintain scalable data pipelines in cloud and hybrid environments
• Contribute to the architecture of enterprise data platforms with a system-oriented mindset
• Work with large-scale datasets using SQL, Spark, and cloud-native tools

• Building transformative AI capabilities from the ground up
• Developing user-facing features with conversational and agentic AI technologies
• Experimenting with generative AI models and techniques
• Tackling challenges at the intersection of AI and enterprise software

• 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

• Architect and implement scalable AI/ML solutions on Azure or GCP
• Design and build AI solutions for sales use cases such as automated RFP/RFI ingestion, classification, and response generation
• Design and build AI solutions for intelligent document understanding

• Architect and implement scalable AI/ML solutions on Azure or GCP
• Design and build AI solutions for sales use cases such as automated RFP/RFI ingestion, classification, and response generation
• Design and build AI solutions for intelligent document understanding

• 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

• Development of highly scalable services realizing AI/ML functionalities
• Implementation of machine learning solutions addressing computer vision and NLP use cases
• Work with Large Language Models (LLMs) and perform prompt engineering tasks