MongoDB Experts in Zurich

• Interface role with the SOC team; improved incident response time by 45% through structured escalation processes and proactive security monitoring.
• Defined technical standards for a multi-tenant enterprise platform (10+ services), resulting in a 30% shorter onboarding time for new clients in the public sector.
• Active knowledge transfer and mentoring of a 6-person team; increased team autonomy and established a sustainable engineering culture.
• Reduced time to market by 50% by optimizing CI/CD pipelines and establishing self-service deployments.
• Stack: GitLab CI/CD, Kubernetes, Nexus, Harbor, ArgoCD, HashiCorp Vault, Grafana, Python, PostgreSQL, Java, Spring Boot, Flyway, Quarkus, JBoss, Loki, Go, Kafka, Kustomize, MongoDB

• Clients: Central banks of the most developed economies in the world, German, Swiss, Luxembourgish, Monegasque, Singaporean, Japanese wealth managers and universal/private banks, world’s largest investment banks
• Accountable for managing the delivery of a portfolio of digital wealth and consumer relationship management initiatives
• Project managing portfolios of projects within CTO product and technology business area (cyber security, architectural changes, re-organisational transformation, 3rd party apps)
• Products: Kernel & Parametrisation for Core Avaloq Banking Platform; modules: CRM, client data management, wealth management, portfolio management (generating over 90% of company revenues)
• Key responsibilities:
• Reporting to Head of Disruptive Innovation Division; stakeholder to five product owners & two domain owners
• Accountable for delivery of annual themes and quarterly key deliverables
• Managing demand vs supply via QBR process: book of work, execution, reporting, risk management
• Directly managing around 40 external consultants (front-end/back-end engineers, BAs, designers)
• Hiring and managing resources in eight countries
• Running quarterly divisional closure meetings presenting achievements and challenges
• Resolving cross-team dependencies and internal/external conflicts
• Owning and controlling maintenance budget; planning, forecasting, execution; ensuring demand matches supply
• Identifying and staffing key roles with chapter leads
• Approach: Agile, Scrum of Scrums, QBR/OKRs
• Technology: Angular, PL/SQL, JavaScript, Confluence, JIRA (JQL)

• Build prototypes, expand AI skills, and practical application in a startup context.
• Transition phase: further training in prompt engineering, APIs, AI, working on independent prototype projects, and collaborating at a friend's startup.
• Technologies: LangChain, REST, Python, Java, JS, TS, Node.

• Greenfield project M-Zollmanager: Development of an internal platform to support users in booking requirement areas on line items in assessment notices.
• Greenfield project M-Deliveryplan: Development of an internal platform for planning and tracking deliveries in the international transport sector.
• Created functions to predict the estimated arrival time of container ships and goods from the port of loading to the port of destination, considering potential events.
• Developed the TrackAndTrace add-on for M-Deliveryplan to track and plan goods and containers.
• Built a generic AI chatbot for Migros app infrastructure, including ChatGPT integration and creating AI workflows with N8N.
• Developed AI assistants to automate repetitive tasks, such as creating release notes or processing Jira tasks based on N8N workflows.
• Further development and maintenance of the M-LFS, M-Vehicleaccess and M-Worklog apps.
• Used C#, Angular 18, DevOps practices, N8N, Kubernetes, Docker, Jira, Confluence and Azure.
• Ongoing project with continuous development of platforms and workflows.

• Supported the team in merging two real estate platforms into a unified Vue.js-based solution
• Developed a custom SSR microservice architecture deployed on AWS Lambdas
• Designed and implemented a map drawing feature
• Took ownership of migrating the microservice from Vue 2 to Vue 3
• Built a scalable SSR microservice architecture using AWS Lambda
• Developed and implemented a critical map drawing feature for real estate visualization
• Successfully migrated the microservice from Vue 2 to Vue 3
• Gained deep knowledge of AWS infrastructure, Datadog, Sentry, and Vite
• Technologies: Vue.js, Node.js, Vite, AWS Lambda, Datadog, Sentry, JavaScript

• Designed large-scale systems and APIs serving thousands of concurrent users.
• Refactored a 650k-LOC monolith and led full AWS migration for stable performance.
• Introduced SLO-based observability, improving reliability and recovery flow.
• Optimised cloud and databases, achieving significant cost and latency reduction.
• Delivered LLM, RAG, and document-automation pipelines adopted in production.

• Deployed and optimized the inference stack on a multi-node DGX B200 cluster across vLLM and SGLang (serving, throughput and latency tuning).
• Built, with a small team, an internal Python library for LM pretraining covering the full training loop: distributed training with PyTorch FSDP, data pipelines, checkpointing, config and hyperparameter management, and experiment tracking.
• Built and evaluated agent scaffolds on interactive game benchmarks similar to ARC-AGI-3, with metrics for how models plan, explore and adapt across multi-step episodes; classified model errors and fed the findings back into scaffold and evaluation design.
• Researched looped transformer architectures.

• Designed and delivered a practical real-time course on Flink and Hadoop with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, and Kafka
• Gained extensive DevOps and CI/CD experience
• Created ETL/ELT pipelines with Apache tools and Pentaho
• Led projects in municipal software, financial services, and big data with Kafka
• Developed AI/NLP models and chatbots with RASA, Chatter, and Dialogflow
• Built and managed a TypeDB knowledge database
• Worked with OpenStack, Kubernetes, and Podman

• Took over the analyst and leading developer role in an innovative project helping detection of financial fraud in a major financial institution in Zurich with assistance of artificial intelligence
• Analyzed and implemented new modules, improved performance and user experience, elevated technical documentation and code quality to the standard required by the end client
• Technologies: Java 17, JavaFX, Spring Boot, concurrency, UX, Gradle, Impala

Application ownership and further development of test user management (TUM)

Areas of responsibility: Application ownership (Application Ownership / ITSO), application management, incident management, deployment / release management, requirements management, prioritization, further development, automation, and interface management

Technologies: SQL Server, Python, GitLab, UC4, Lieberman, Linux

Responsible for the operation and continuous development of a central application for managing more than 5.000 technical test users and their data.

Gathered and assessed business requirements, independently prioritized further development, and planned and coordinated implementation using agile methods and PI Planning, in close collaboration with the relevant business units and IT teams.

Expanded the central SQL Server database with additional information about test and real users, and integrated further applications through defined interfaces. The modular application used Python-based processing components, with execution and workflows controlled through UC4. This enabled data from different source systems to be automatically transferred, processed, and made available, as well as new test users to be created automatically.

Ensured reliable application operations and continuously optimized and automated existing processes.

Application ownership, modernization, and migration of incident management (TRANT)

Areas of responsibility: Application ownership (Application Ownership / ITSO), application management, incident management, deployment / release management, requirements management, software modernization, migration, interface integration, and quality assurance

Technologies: UC4, Jira, SQL Server, Oracle, Java, Python, Lieberman, Linux, Jai

Responsible for the operation and development of an application that automatically records, processes, and forwards technical incidents in various test environments.

Automated the creation of Jira incident tickets when UC4 workflows were blocked, including routing them to the relevant application managers.

Modernized the application by migrating from Java to Python, moving the database from Microsoft SQL Server to Oracle, and replacing the Jenkins scheduler with UC4.

Integrated the Jira API and used the bank's internal AI solution Jai to support code migration and subsequent validation. Independently and critically reviewed AI-generated results for business and technical accuracy.

Shift lead for operations control in test environments

Areas of responsibility: Monitoring various test systems

Technologies: UC4, TSO, HOST, Azure-Cloud

Responsible for monitoring and controlling automated processing workflows in various test environments, including systems running in the Azure-Cloud.

Monitored UC4 and HOST workflows and analyzed and handled incidents. When processing workflows were blocked, Jira tickets were automatically created through TRANT and assigned to the relevant application owners. In coordination with them, affected jobs were restarted or stopped as needed. HOST processes were also monitored and controlled through OPC.

A multi-shift operation was introduced to ensure stable and as continuous as possible testing.

Test data anonymization manager

Areas of responsibility: Planning/anonymizing/publishing production data for test systems

Technologies: UC4, Informatica (PowerCenter and IDMC)

To ensure test environments could run with real data, production databases were cloned regularly. Production data had to be untraceable in test environments. For this reason, the cloned databases were first anonymized with Informatica PowerCenter and later with Informatica IDMC. Critical tables and columns in the various databases (Oracle, SQL-Server, MongoDB) had to be identified and analyzed at the data level. Rules for handling critical data were agreed with the application owners.