Recommender System Experts in Berlin

• Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
• Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
• Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
• Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
• Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
• Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
• Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
• Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
• Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
• Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices

• Built a JTBD-based prioritization framework for 3,000+ accumulated feature requests, identified 27 broker jobs, validated 8 through 25 user interviews, and used the resulting job map as a live prioritization filter for all incoming channels (Upvoty, CSAT, consulting tickets).
• Responsible for the Scout24 Lighthouse initiative: Document Intelligence with full RAG architecture (semantic chunking, bge-m3 embeddings, pgvector, BM25+Dense hybrid retrieval).
• Reduced lead time of customer feature requests to 3.1 days through code analysis, ticket specification, and independent implementation using a coding agent (Codex).
• Developed an LLM-based support agent (GPT-4o mini, Codex-generated merge requests) that reduced 3rd-level escalations from 40% to 5% of all monthly tickets.
• Integrated six partners through technical coordination, specification, backlog and release management, and led seven full stack developers.
• Eliminated regulatory exposure for brokers in six weeks through risk analysis (BGH ruling on distance selling/GDPR), new audit features, and coordination with legal and data protection officers.

• Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
• Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
• Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
• Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
• Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow

• Setup: retail group in transition, agile environment, team with 10 developers
• Goal: replacement of a software monolith, redesign of static content, structuring of further backoffice portals
• Challenge: complex system-side dependencies, stakeholders for software development & digitalization
• Tasks: subject-matter responsibility for static content and operational processes

• Attempted founding of a platform for career development and personal coaching
• Top 3 placement in the Berlin-Brandenburg business plan competition
• Conducted independent market analysis and user research
• Built a comprehensive knowledge graph for roles, skills, and experiences
• Data transformation and setting up data pipelines on Azure

• Developed a Generative AI-based image recommendation engine for an AI video production system
• Implemented automatic image analysis with GPT-4o
• Built semantic vector search using OpenAI embeddings, MongoDB, and OpenSearch
• Tagged and indexed 4 million customer assets
• Redeveloped recommendation engine with a hybrid, balanced keyword and vector search plus filters

• Led a 25+ team product organisation, driving alignment, transparency, and operational efficiency.
• Introduced new collaboration processes with stakeholders to improve cross team communication and decision making.
• Established a cross functional peer mentoring program to strengthen knowledge sharing and leadership development.

• Defined and executed software product strategy, driving seamless integration with IoT devices
• Improved platform capabilities such as usability, performance (time taken to unlock, successful unlocks, reducing error rate), security and customer satisfaction across connected devices
• Rebuilt the platform to scale by overhauling the underlying technology of the mobile app and improving developer experience, enabling the team to efficiently develop and deploy changes without technical limitations

• Built an FRT (Full Resolution Time) data product in dbt and BigQuery with a MECE ticket lifecycle methodology derived from a unified semantic mapping and ordered event stream.

• Delivered reusable macros, modular models, automated unit tests, and a LookML metric layer adopted by Process Improvements and Ops.

• Overhauled FRT experiments using quasi-experimental and pre-post causal analyses to demonstrate that slower resolution affected GMV, enabling shifting from a blanket 70%-in-48h SLA to problem-specific targets and providing the analytical foundation for SLA redesign.

• Owned the analytics stack end-to-end across data modeling, cloud setup, access control, cost management, and stakeholder-facing dashboards.
• Built and maintained large-scale data workflows across 20+ APIs and 100M+ rows using GCP, BigQuery, dbt, and Spark.
• Supported product, marketing, finance, and commercial teams with KPI frameworks, reporting layers, and decision support.
• Introduced automation and AI-assisted analytics use cases to improve insight generation and internal workflows.

• Support the implementation and monitoring of the company’s quality standards to ensure that every product meets the user’s needs and requirements

• Create a dashboard in Excel for monitoring the Cost of Poor Quality which connected data from seven (7) KPIs

• Manage error and issues tracking with JIRA coordinating with engineering and production to conduct root cause analysis and corrective actions

• Collaborate with production and application teams to resolve bugs/issues

Geeks Ltd is a UK-based technology company; WordUp is its AI-driven language-learning product focused on personalized vocabulary learning and intelligent educational experiences.

• Coordinate AI product delivery across Product, Engineering, Data, Operations, and leadership, translating user needs into scoped initiatives, sequencing work, surfacing blockers, facilitating hand-offs, and communicating progress.
• Own search, recommendation, retrieval, and content-enrichment features end to end, from requirements and architecture through Python/FastAPI implementation, testing, deployment, monitoring, and rapid iteration.
• Developed low-latency retrieval, ranking, and personalization services using AWS, OpenSearch, DynamoDB, embeddings, and reusable APIs, achieving <1s latency, 22% higher engagement, and 12% higher premium conversion.
• Use AI coding assistants for codebase analysis, scaffolding, refactoring, tests, debugging, and documentation while reviewing every output for correctness, architectural fit, security, maintainability, and user value.
• Represent technical work in planning and stakeholder discussions, gather requirements first-hand, challenge priorities constructively, explain delivery trade-offs, and help teammates make outcome-focused decisions.

• Train and optimize MAVAE/VAE models in PyTorch to detect anomalies in multivariate time-series sensor data.
• Design preprocessing workflows and evaluation pipelines to improve model accuracy and robustness.
• Benchmark MAVAE performance against baseline statistical and deep learning approaches, presenting comparative insights.
• Collaborate with research supervisors to refine hypotheses and translate experimental findings into deployable research outputs.

• Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search.