Python programmer állás, munka

• Experience in different data science topics
• Independent delivery of data science projects with product-oriented mindset
• Engagement with clients to understand business objectives and translate into technical requirements
• Leading pre-sales technical discussions

• Support the management of user permissions across multiple tools and systems including AWS IAM
• Help maintain and improve security tooling including endpoint protection solutions and infrastructure monitoring tools
• Assist with monitoring security alerts and investigating unusual events

• Design of evaluation framework for scalable LLM-as-a-judge pipelines
• Development of rubrics and curation of high-quality golden datasets
• Optimization of judge prompts for automated scoring
• Full-stack product development contribution
• Integration of automated evaluations into CI/CD pipelines

• Design of complex and scalable data solutions on the AWS cloud platform
• Development and implementation of complex and scalable data solutions on the AWS cloud platform
• Design and optimization of data pipelines
• Writing clean, efficient and maintainable SQL and Python code

• Architect scalable inference services, APIs, and backend components for model-driven and LLM-powered product features
• Productionize AI and ML workflows with strong MLOps practices
• Define and implement evaluation frameworks for model quality, system reliability, latency, and cost

• Design, build, and ship production retrieval systems
• Own vector store selection and operation including scalability, latency, reliability, cost, and multi-tenant design
• Build and operate indexing and refresh pipelines including chunking, embedding generation, backfills, deletes, and versioned indices

Design and build knowledge tracing and longitudinal learner models that support mastery and progression features surfaced to customers...

• 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, 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

• Lead the end-to-end implementation of data pipelines and GenAI applications on Databricks
• Design and maintain CI/CD, model serving, monitoring, and evaluation frameworks for AI production reliability
• Tune complex Spark jobs and LLM inference patterns for maximum efficiency and minimum latency

• Leading complex penetration testing engagements
• Working with enterprise-scale cloud and Kubernetes environments
• Mentoring other security professionals
• Helping clients understand and reduce real-world cyber risks

• Monitoring configuration, maintenance and usage of environments with virtual machines and cloud based infrastructures (Kubernetes)
• Cooperation with platform engineers, software architects, delivery teams and operational units

• 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 including IaC scanning and policy-as-code

• Definition and development of system-level tests for high-end embedded computing systems
• Automation of test execution and evaluation through scripting in a CI environment
• Increase of test coverage by extending functional examples into comprehensive test suites

• Implementation and maintenance of large-scale data pipelines and the Azure/Databricks data platform
• Building robust, scalable pipelines using Azure Databricks (PySpark / SQL)
• Integration of data ingestion, processing, transformation, and storage

• 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

• Co-design and implement QA workflows using statistical diagnostics, validation rules, and anomaly detection techniques
• Application of AI/machine learning tools to identify inconsistencies, missing values, or suspicious patterns in large-scale asset datasets

Lead, Plan, and Deliver: You will own the technical execution for the AI team. This includes translating high-level user stories into clearly defined technical work packages, creating robust delivery plans in close cooperation with the SW Engineering Team and your own team, and ensuring timely,...