RIK JANSEN

- Designed a machine learning prediction platform for SaaS operations using Python 3.9–3.12, PyTorch 1.12–2.x, FastAPI 0.95–0.110, and MLflow 2.x, enabling product teams to deploy scoring services with consistent versioning and rollback support. - Built real-time anomaly detection pipelines with Kafka 3.x, Redis 7.x, PostgreSQL 14–16, and Kubernetes 1.24–1.29, reducing incident detection delay by 27% across high-volume operational workflows. - Migrated legacy batch-based scoring jobs from Flask 1.x and cron scripts into FastAPI, Airflow 2.x, and containerized services, improving model refresh reliability by 23% while removing hidden manual deployment steps. - Implemented RAG-based document intelligence features using Hugging Face Transformers 4.x, OpenAI API, vector embeddings, and Elasticsearch 8.x, allowing support teams to retrieve policy and ticket context faster. - Resolved a production issue where inconsistent feature normalization caused model confidence drift, adding feature-store validation, MLflow comparison reports, and automated regression checks before release. - Developed OpenAPI-based ML inference endpoints with JWT authentication, request validation, structured logging, and rate limiting, improving API stability for internal and partner-facing applications. - Introduced model monitoring dashboards with Prometheus, Grafana, and custom Python metrics to track latency, prediction distribution, failed inference calls, and data quality degradation across environments. - Optimized model serving containers by reducing unused dependencies, caching tokenizer assets, and tuning worker concurrency, cutting average inference latency by 19% under peak traffic conditions. - Collaborated with backend, data, and product teams to implement role-based AI features, ensuring sensitive predictions and audit explanations were visible only to authorized users. - Improved CI/CD for ML models using GitHub Actions, Docker 24–25, Kubernetes deployments, and MLflow registry promotion rules, making staging-to-production releases more predictable and traceable. - Fixed a memory leak in a long-running embedding service by profiling tensor lifecycle, batching vectorization calls, and clearing stale request objects after asynchronous processing completed. - Created internal AI engineering guidelines for prompt safety, model versioning, dataset lineage, and rollback strategy, helping developers use AI/ML tooling more flexibly without increasing production risk.

- Developed machine learning APIs for customer analytics and workflow automation using Python 3.7–3.9, TensorFlow 2.x, scikit-learn 0.22–0.24, Flask 1.x–2.x, and PostgreSQL 11–13. - Built classification and ranking models for business process prioritization, improving internal decision accuracy by 21% through better feature engineering and cross-validation workflows. - Migrated older Python 3.6 services to Python 3.8–3.9 with dependency upgrades, test coverage improvements, and Docker-based runtime consistency across development and production environments. - Implemented scheduled data pipelines with Airflow 1.10–2.x, Pandas 1.x, SQLAlchemy, and S3-compatible object storage to prepare training datasets from operational application data. - Resolved an issue where duplicate records were inflating model metrics by adding data quality checks, deduplication logic, and validation reports before training jobs were executed. - Integrated TensorFlow 2.x model serving behind Flask and REST APIs, allowing backend systems to consume predictions without direct dependency on training code or raw datasets. - Improved slow recommendation queries by indexing high-cardinality fields, caching frequent lookups in Redis 5.x–6.x, and reducing average response time by 18% for core workflows. - Created model evaluation dashboards showing precision, recall, confusion matrix trends, and dataset coverage, helping product stakeholders understand prediction trade-offs before release. - Worked on NLP-based text classification for support messages using TF-IDF, word embeddings, and early Transformer-based experiments where they were realistic for production usage. - Implemented Docker-based CI pipelines with GitLab CI and Jenkins, ensuring Python tests, linting, API checks, and model validation completed before deployment approval. - Refactored tightly coupled ML scripts into reusable services and shared Python modules, improving maintainability and making future feature delivery faster across multiple product teams.

- Built data-driven backend services using Python 3.5–3.6, Django 1.11, Flask 0.12–1.x, scikit-learn 0.18–0.20, PostgreSQL 9.x–10.x, and JavaScript ES6 for digital platforms. - Implemented early customer segmentation models with scikit-learn pipelines, clustering, and SQL-based feature extraction, helping marketing teams improve campaign targeting by 16%. - Migrated reporting logic from manual spreadsheet exports into automated Python and PostgreSQL workflows, reducing recurring data preparation time by 24% for internal analysts. - Created REST endpoints for analytics dashboards that exposed campaign performance, user behavior, and prediction outputs through secure backend services and structured JSON responses. - Fixed a recurring issue where timezone mismatches caused incorrect daily metrics by normalizing timestamps in ETL scripts and adding database-level validation rules. - Implemented basic recommendation logic using historical interaction data, weighted scoring, and SQL aggregation before later machine learning workflows became more standardized. - Improved data import reliability by adding retry logic, error logging, malformed row handling, and clear failure reports for CSV and third-party API ingestion jobs. - Worked with designers and product stakeholders to translate business questions into measurable data features, reports, and lightweight predictive models for client-facing products. - Optimized slow analytics pages by rewriting inefficient joins, adding indexes, and caching repeated calculations, improving dashboard load speed by 20% on large accounts. - Contributed to code reviews and deployment preparation for Python services, ensuring model-related scripts followed consistent structure, testing, and operational documentation.